The ITV presenter sat down with Arnold Schwarzenegger to discuss his new book 'Be Useful Seven Tools For Life'.

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A koala out for a stroll was escorted off the road after it brought traffic to a halt in Burleigh Heads, Queensland.Burleigh Heads resident Robertha Cavalcanti said she was travelling on Peggs Road when she came across the standstill.

'Average Aussie Day' as Woman Escorts Koala Across Gold Coast Road

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THE MORNING of October 19 started like any other for Blake Moon and his partner - they woke up, they took their dogs outside, and welcomed the day.

Yuki the koala was found at Blake Moon's doorstep near Rockley

Filmed last Friday morning, the short clip below shows a large male koala sitting in the car docking bay. “Come on, off the ferry. Do you have your pass on you?” he jokes. The koala doesn’t move. Lachie lets out a low sigh.

Ferry driver surprised by koala stowaway: 'What are you doing here?'

The charity’s president Shelley Robinson told Yahoo the animals can be frightened by dogs, people or loud noises, causing them to flee towards the water. “They end up in ferries, yachts, and catamarans,” she said.

Aussie Ferry driver surprised by koala stowaway: 'What are you doing here?'

MYALL Coast residents gathered at a hastily-arranged meeting to discuss proposed offshore wind farms on Thursday 26 October at Hawks Nest Community Hall. Speakers included the Hawks Nest Tea Gardens Progress Association (HNTGPA),

Myall Coast residents meet to discuss wind farm situation with Federal MP

SYDNEY, Nov. 1, 2023 /PRNewswire/ — Visa , a world leader in digital payments, is proud to have entered into a long-term partnership with Taronga Conservation Society Australia, becoming the preferred payment partner at Taronga Zoo in Sydney and Taronga Western Plains Zoo in Dubbo.

Visa partners with Taronga Conservation Society Australia to support wildlife conservation

The Australian Koala Foundation (AKF) has announced a multi-year sponsorship of the skilled junior wheelchair sportsman Benjamin Wenzel. Ranked number six in the world and number one in Australia in junior boy’s wheelchair tennis,

The Australian Koala Foundation Sponsors Junior Wheelchair Tennis Star

From a biologist to a charity founder, the South Australian winners for the 2024 Australian of the Year have been announced. See who has just been crowned. Four trailblazers from our state have been crowned SA’s Australians of the Year for 2024.

South Australia’s 2024 Australian of the Year winners have been announced

Koala

koala

The Red List of the International Union for Conservation of Nature, the authority on the conservation status of the world's species, assures that the koala population is in the process of decline and, therefore, it is vulnerable to extinction. In 2016, experts estimated a population of around 330,000 koalas, which represented an average decrease of 24% in the population compared to the three previous generations. Before the fires in Australia in 2020, there was already talk that this species was "functionally extinct", a term coined by the Australian Koala Foundation (AKF) in a statement to affirm that there was no more than 80,000 Koalas in the country. During the fires in Australia, images started to appear of defenseless koalas before the flames were about to be rescued. According to local estimates, between 350 and 1,000 dead koalas were found during the fires. A problem that can be solved The koala is of great importance within the Australian ecosystem. For millions of years it has been key to the health of eucalyptus forests. This is because they eat the upper leaves of these trees and their excrement helps the recycling of nutrients in the soil. Therefore, if the koala population decreases and stops performing these functions, the ecosystem will suffer. Their vulnerability to fires is evident, since their only way out is to climb towards the top of the eucalyptus trees in which they live, which were severely  devastated by the 2020 forest fires, due to global warming. With COP 26 on the rise and new efforts to lower the earth's temperature by 1.5 degrees, attempts to reverse this situation will take time to materialize, if there really is political will to do so. In this sense, the time factor is vital here to guarantee the survival of endangered species such as koalas; the main reason that has prompted the artists of the KRebels project to generate a unique collection of NFTs to protect this defenseless animal. Enters: KRebels - Koala NFTs KRebels is the first project to use non-fungible tokens (NFTs) issued through blockchain technology to raise awareness of the dangers inherent in climate change and its devastating effects on vulnerable species such as Australian koalas. 9,999 works of art tokenized on the Ethereum blockchain inspired by koalas are part of the theme of this collection presented by the KRebels Project, in order to generate the necessary resources to support foundations in charge of safeguarding this species, at the same time as raising awareness in the digital art loving crypto community. KRebels is an innovative NFT project focusing on the intersection of cutting-edge technology, art, and saving the endangered Koala. With KRebels NFTs it will be possible to preserve koalas beyond the virtual world, extrapolating these species to a virtual environment such as metaverses; where holders of these rare and unique NFTs will be able to use it in digital koala reserves on the Metaverse Blockchain. NFTs to raise awareness for endangered species The market for non-fungible tokens (NFT) is on the rise, although it may seem difficult to understand for most newbies. However, interest continues to grow in and out of this ecosystem as more artists, musicians, collectors and investors of all sizes converge in this digital world. According to , there are currently more than 500 million dollars in sales in this market, at a daily average of $ 2,666 of daily sales among the different digital collectibles issued on the blockchain, including everything from the arts to metaverses. NonFungible.com KRebels wants to take advantage of this ‘euphoria’ around NFTs to raise awareness in a sustainable project that can serve as inspiration for the preservation of the species through the fun use of digital collectibles with utility within the blockchain ecosystem. The NFT collection of koalas is the first step of an ambitious project that aims to touch the most sensitive fibers of the crypto community and especially, of those who believe in the possibility that blockchain technology can help change the world into one in which species diversity is possible. The collectibles market is proving to be a vital aspect of the growth of NFTs. And KRebels is developing a solid foundation to become a major player in this market segment. With the KRebels NFTs, digital art collectors will be able to experience the power of blockchain technology while supporting a noble cause, which from the investor's point of view, can be an asset with great potential in the immediate future given the originality of the concept and innovation of combining technology with structural problems of daily life such as endangered species. How does it work? KRebels foresees in five simple steps the minting of NFTs on the Ethereum blockchain through a simple and powerful interface of digital works of art that can then be commercialized in decentralized marketplaces such as OpenSea. The goal of the platform is to allow holders of digital koalas to participate in educational awareness events about this species while at the same time using their tokenized collectibles to play and be able to win cryptocurrencies inside and outside the metaverses. To do this, its roadmap is based on six key points: 1.- : As the owner of a koala NFT, you will have participation rights and a voice in the management of the project and improvements to be implemented, in the best style of decentralized governance. Community 2.- : The KRebels project has committed to giving a percentage of its profits to projects focused on the preservation of koalas. Charity 3.- : As we have mentioned previously, the KRebels NFT koalas will be able to be used in the future in digital reserves within the blockchain metaverse. New World 4.- : Within the metaverses, holders of the Koala NFT can use them in games within this virtual world, which will allow them to earn cryptocurrencies with them. Video games 5.- : KRebels foresees the launch of a token and a new collection after the launch of the first generation of KRebels NFTs as a way to reward its community. Airdrop 6.- : As part of the sustainability of the project, KRebels foresees a percentage of the royalty fees being returned to the community, in order to generate a circular economy with the holders of their NFTs. Long life

These NFTs Might Help Save the Koalas, a Species in Danger

MemeCore, a leading EVM-based Layer 1 multi-chain cross-staking blockchain secured by the innovative Proof of Meme (PoM) consensus mechanism, together with 852Web3, Pudgy Penguins, Hashkey Exchange, and TON, is thrilled to announce the upcoming “MemeX Festival: Be Yourself, Be Memorable.” Scheduled to take place on February 18, 2025, during Consensus HK 2025, the festival aims to foster collaboration between Web2 and Web3 projects, enhance brand visibility for MemeCore’s flagship project MemeX, and solidify MemeCore’s position as the premier playground for meme-related initiatives in the blockchain ecosystem. With over 2,000 registrations already confirmed, the event is set to be a landmark gathering for blockchain and meme enthusiasts alike. Bridging Web2 and Web3: The Future of Collaborative Innovation The MemeX Festival is a pioneering initiative designed to bridge the gap between Web2 and Web3, fostering collaboration and innovation across both domains. By bringing together traditional and blockchain-based projects, the festival aims to drive meaningful partnerships that leverage the strengths of each ecosystem. As part of this effort, MemeX will take center stage, showcasing its groundbreaking platform that seamlessly integrates social media, a bonding curve launchpad, and sponsorship mechanisms. With Consensus HK as its backdrop, the festival serves as a strategic opportunity to elevate MemeX’s brand presence, demonstrating its ability to transform user engagement into tangible value creation while fostering sustainable growth within the Web3 space. Event Highlights Luxurious Venue: The Kerry Hotel - The MemeX Festival promises an unforgettable experience at the Kerry Hotel, a venue renowned for its high-class and elegant atmosphere, offering stunning views of Hong Kong's iconic Victoria Harbour. This luxurious setting will serve as the perfect backdrop for a celebration of innovation and culture, blending sophistication with a touch of nostalgia. Neon Nostalgia: 90s Hong Kong Vibes - To make the event truly unique, the festival will incorporate the vibrant energy of 90s Hong Kong, featuring neon lights and cosplay of iconic characters from the era. This creative fusion of elegance and nostalgia will provide an immersive and memorable experience, ensuring attendees leave with a sense of excitement and delight. \Interactive Fun and Rewards - Beyond the captivating atmosphere, attendees can enjoy nostalgic carnival games that bring back the magic of childhood memories. These games are designed to engage guests actively while offering exclusive, festival-only merchandise as rewards, making the experience both fun and memorable. MemeX Festival: Be Yourself, Be Memorable Event Partners Co-hosts: MemeCore, MemeX, Pudgy Penguins, Hashkey Exchange, TON, 852Web3 Strategic Partners: ARC Community, BlockBooster, BitRush, Weekly Dose, Memecoin Summit Sponsored Partners: MEW, The Shib, Neiro, MoonRing, One Earth, Yuliverse, The Moo, Shifu The Panda, Kiki, Metacade, Nobody, Banana, Pureverse, Wonton, DTK, Andy On Bsc, BAEBOT, Card3, Courage, Dogelon, Fautor, Gate Web3 Memebox, Koala AI, RichQuack, SquidGrow, WHY, XRDoge, Pink on TON, BananaS31\ Meme Partners: 4Chan, ADA on SOL, Awebo, Barsik, Big Red, Bitchcoin, Byte, Catcoin, Catlorian, Cultiv8, Daram, Dougy, Elawn Moosk, FEG, GmestopSol, HANA, High as Unicorn, In The Meme Time, Kishu, MiniDoge, Mystery, NOOZ, OG Ginnan, OG Peanut, Pedro, Pepe on Sui, Piggy, Pitbull, POPDOG, QWACK, Retail DAO, Sendor, Sharbi, TWIGGY, Volt Inu, Wenwifhat, WHALE, Who Is Ebert, XPX, Xviral, YiLongMa, ZIPPO \Media Partners: MetaEra, Foresight News, PA News, ODaily, Tokenpost, Jinse Finance, Blocktempo, MonsterBlock HK, CryptoPie, Pacific Meta, Web3+, CryptoTW, 852Web3 Magazine, DTC Group, 0x Consulting Group, Coinness, Blockchain of the Day, Blockcast \MemeX Festival Kickoff Party\ Prior to the festival, MemeCore hosted the MemeX Festival Kickoff Party, which took place at the stunning Baijiu Master at K11 Musea in Hong Kong. This exclusive event marked the beginning of the highly anticipated MemeX Festival: Be Yourself, Be Memorable, bringing together a vibrant community of blockchain enthusiasts, meme coin supporters, and Web3 innovators. The Kickoff Party set the stage for the larger MemeX Festival, scheduled for February 18, 2025, and provided a dynamic platform for networking, collaboration, and celebration. MemeX Festival Press Conference\ In preparation for the festival, MemeCore hosted a Press Conference on February 7, 2025, at K11 Atelier in Hong Kong. This event’s purpose was to formally introduce the MemeX Festival and provide comprehensive background information about its hosts, MemeCore, MemeX, Pudgy Penguins, TON, Hashkey Exchange and 852Web3. The Press Conference also offered valuable networking opportunities for members of the blockchain community and Web2 projects, setting the stage for the main event in February., ** Register Now to Join the MemeX Festival Don’t miss out on this one-of-a-kind event that blends innovation, culture, and fun! Be part of the MemeXFestival on February 18, 2025, at the prestigious Kerry Hotel during Consensus HK 2025. With over 2,000 registrations already confirmed, this is your chance to connect with Web2 and Web3 innovators, experience the vibrant 90s Hong Kong nostalgia, and enjoy exclusive festival-only perks and activities. 👉 Secure your spot now by registering here: https://lu.ma/memexfest Spaces are filling up quickly—don’t wait to join this unforgettable celebration! About MemeX MemeX is at the forefront of merging social media with blockchain technology. By combining a social media platform, a bonding curve launchpad, and sponsorship models, MemeX creates an ecosystem where user engagement fuels economic value. Users can share content that sparks interactions, with each token associated with profiles or projects appreciating in value through bonding curve sales. This innovative structure ensures the platform's sustainability, allowing communities to grow and thrive beyond their initial launch periods. MemeX bridges the gap between social interaction and economic opportunity, fostering enduring community growth and engagement. About MemeCore MemeCore Foundation is the visionary organization behind the MemeCore blockchain, an EVM-based Meme Chain, Layer 1 secured by the unique Proof of Meme (PoM) consensus mechanism. Specifically engineered for meme coins, MemeCore aspires to cultivate a dynamic playground for meme communities within the broader blockchain ecosystem. With a mission to foster a memeable space that champions creativity, inclusiveness, and innovation, MemeCore leverages its distinctive PoM consensus to empower the meme coin community. Dedicated to promoting a decentralized future, MemeCore ensures top-tier security and provides engaging services that resonate with meme coin enthusiasts worldwide. For more information about MemeCore and its initiatives, please visit https://memecore.com/. MemeCore Foundation MemeCore Foundation is the organization responsible for developing the MemeCore blockchain, an EVM-based Meme Chain, Layer 1 network secured by the Proof of Meme (PoM) consensus mechanism. Our mission is to elevate the fun within the blockchain space, encapsulated by our motto: "Lift up the Fun! $M." Contact Information For media inquiries and further information, please contact: Josh Kwon Marketing Specialist MemeCore Foundation Email: josh@memecore.com Website: https://memecore.com/ This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here MemeCore, a leading EVM-based Layer 1 multi-chain cross-staking blockchain secured by the innovative Proof of Meme (PoM) consensus mechanism, together with 852Web3, Pudgy Penguins, Hashkey Exchange, and TON, is thrilled to announce the upcoming “MemeX Festival: Be Yourself, Be Memorable.” Scheduled to take place on February 18, 2025, during Consensus HK 2025, the festival aims to foster collaboration between Web2 and Web3 projects, enhance brand visibility for MemeCore’s flagship project MemeX, and solidify MemeCore’s position as the premier playground for meme-related initiatives in the blockchain ecosystem. With over 2,000 registrations already confirmed, the event is set to be a landmark gathering for blockchain and meme enthusiasts alike. Bridging Web2 and Web3: The Future of Collaborative Innovation The MemeX Festival is a pioneering initiative designed to bridge the gap between Web2 and Web3, fostering collaboration and innovation across both domains. By bringing together traditional and blockchain-based projects, the festival aims to drive meaningful partnerships that leverage the strengths of each ecosystem. As part of this effort, MemeX will take center stage, showcasing its groundbreaking platform that seamlessly integrates social media, a bonding curve launchpad, and sponsorship mechanisms. With Consensus HK as its backdrop, the festival serves as a strategic opportunity to elevate MemeX’s brand presence, demonstrating its ability to transform user engagement into tangible value creation while fostering sustainable growth within the Web3 space. Event Highlights Luxurious Venue: The Kerry Hotel - The MemeX Festival promises an unforgettable experience at the Kerry Hotel, a venue renowned for its high-class and elegant atmosphere, offering stunning views of Hong Kong's iconic Victoria Harbour. This luxurious setting will serve as the perfect backdrop for a celebration of innovation and culture, blending sophistication with a touch of nostalgia. Neon Nostalgia: 90s Hong Kong Vibes - To make the event truly unique, the festival will incorporate the vibrant energy of 90s Hong Kong, featuring neon lights and cosplay of iconic characters from the era. This creative fusion of elegance and nostalgia will provide an immersive and memorable experience, ensuring attendees leave with a sense of excitement and delight. \Interactive Fun and Rewards - Beyond the captivating atmosphere, attendees can enjoy nostalgic carnival games that bring back the magic of childhood memories. These games are designed to engage guests actively while offering exclusive, festival-only merchandise as rewards, making the experience both fun and memorable. MemeX Festival: Be Yourself, Be Memorable Event Partners Co-hosts: MemeCore, MemeX, Pudgy Penguins, Hashkey Exchange, TON, 852Web3 Strategic Partners: ARC Community, BlockBooster, BitRush, Weekly Dose, Memecoin Summit Sponsored Partners: MEW, The Shib, Neiro, MoonRing, One Earth, Yuliverse, The Moo, Shifu The Panda, Kiki, Metacade, Nobody, Banana, Pureverse, Wonton, DTK, Andy On Bsc, BAEBOT, Card3, Courage, Dogelon, Fautor, Gate Web3 Memebox, Koala AI, RichQuack, SquidGrow, WHY, XRDoge, Pink on TON, BananaS31\ Meme Partners: 4Chan, ADA on SOL, Awebo, Barsik, Big Red, Bitchcoin, Byte, Catcoin, Catlorian, Cultiv8, Daram, Dougy, Elawn Moosk, FEG, GmestopSol, HANA, High as Unicorn, In The Meme Time, Kishu, MiniDoge, Mystery, NOOZ, OG Ginnan, OG Peanut, Pedro, Pepe on Sui, Piggy, Pitbull, POPDOG, QWACK, Retail DAO, Sendor, Sharbi, TWIGGY, Volt Inu, Wenwifhat, WHALE, Who Is Ebert, XPX, Xviral, YiLongMa, ZIPPO \Media Partners: MetaEra, Foresight News, PA News, ODaily, Tokenpost, Jinse Finance, Blocktempo, MonsterBlock HK, CryptoPie, Pacific Meta, Web3+, CryptoTW, 852Web3 Magazine, DTC Group, 0x Consulting Group, Coinness, Blockchain of the Day, Blockcast \MemeX Festival Kickoff Party\ Prior to the festival, MemeCore hosted the MemeX Festival Kickoff Party, which took place at the stunning Baijiu Master at K11 Musea in Hong Kong. This exclusive event marked the beginning of the highly anticipated MemeX Festival: Be Yourself, Be Memorable, bringing together a vibrant community of blockchain enthusiasts, meme coin supporters, and Web3 innovators. The Kickoff Party set the stage for the larger MemeX Festival, scheduled for February 18, 2025, and provided a dynamic platform for networking, collaboration, and celebration. MemeX Festival Press Conference\ In preparation for the festival, MemeCore hosted a Press Conference on February 7, 2025, at K11 Atelier in Hong Kong. This event’s purpose was to formally introduce the MemeX Festival and provide comprehensive background information about its hosts, MemeCore, MemeX, Pudgy Penguins, TON, Hashkey Exchange and 852Web3. The Press Conference also offered valuable networking opportunities for members of the blockchain community and Web2 projects, setting the stage for the main event in February., ** Register Now to Join the MemeX Festival Don’t miss out on this one-of-a-kind event that blends innovation, culture, and fun! Be part of the MemeXFestival on February 18, 2025, at the prestigious Kerry Hotel during Consensus HK 2025. With over 2,000 registrations already confirmed, this is your chance to connect with Web2 and Web3 innovators, experience the vibrant 90s Hong Kong nostalgia, and enjoy exclusive festival-only perks and activities. 👉 Secure your spot now by registering here: https://lu.ma/memexfest Spaces are filling up quickly—don’t wait to join this unforgettable celebration! About MemeX MemeX is at the forefront of merging social media with blockchain technology. By combining a social media platform, a bonding curve launchpad, and sponsorship models, MemeX creates an ecosystem where user engagement fuels economic value. Users can share content that sparks interactions, with each token associated with profiles or projects appreciating in value through bonding curve sales. This innovative structure ensures the platform's sustainability, allowing communities to grow and thrive beyond their initial launch periods. MemeX bridges the gap between social interaction and economic opportunity, fostering enduring community growth and engagement. About MemeCore MemeCore Foundation is the visionary organization behind the MemeCore blockchain, an EVM-based Meme Chain, Layer 1 secured by the unique Proof of Meme (PoM) consensus mechanism. Specifically engineered for meme coins, MemeCore aspires to cultivate a dynamic playground for meme communities within the broader blockchain ecosystem. With a mission to foster a memeable space that champions creativity, inclusiveness, and innovation, MemeCore leverages its distinctive PoM consensus to empower the meme coin community. Dedicated to promoting a decentralized future, MemeCore ensures top-tier security and provides engaging services that resonate with meme coin enthusiasts worldwide. For more information about MemeCore and its initiatives, please visit https://memecore.com/. MemeCore Foundation MemeCore Foundation is the organization responsible for developing the MemeCore blockchain, an EVM-based Meme Chain, Layer 1 network secured by the Proof of Meme (PoM) consensus mechanism. Our mission is to elevate the fun within the blockchain space, encapsulated by our motto: "Lift up the Fun! $M." Contact Information For media inquiries and further information, please contact: Josh Kwon Marketing Specialist MemeCore Foundation Email: josh@memecore.com Website: https://memecore.com/ This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here her e her

MemeCore Announces “MemeX Festival: Be Yourself, Be Memorable” To Bridge Web2 And Web3 Communities 

MemeCore “MemeX Festival፡ እራስህን ሁን፣ የማይረሳ ሁን” ወደ ድልድይ ድር2 እና ድር3 ማህበረሰቦች ያስታውቃል። 

أعلنت MemeCore عن "مهرجان MemeX: كن نفسك، كن مميزًا" لربط مجتمعات Web2 وWeb3

MemeCore najavljuje “MemeX festival: budi svoj, budi nezaboravan” kako bi premostio Web2 i Web3 zajednice 

MemeCore anuncia "MemeX Festival: Be Yourself, Be Memorable" per connectar les comunitats Web2 i Web3 

MemeCore anuncia el “Festival MemeX: Sé tú mismo, sé memorable” para unir las comunidades de la Web2 y la Web3

MemeCore najavljuje “MemeX Festival: Budi svoj, budi nezaboravan” za premošćivanje Web2 i Web3 zajednica 

MemeCore annuncia “MemeX Festival: sii te stesso, sii memorabile” per unire le comunità Web2 e Web3

MemeCore が Web2 と Web3 コミュニティをつなぐ「MemeX フェスティバル: 自分らしく、記憶に残る」を発表

MemeCore ປະກາດ "ງານບຸນ MemeX: ຕົວທ່ານເອງ, ເປັນທີ່ຫນ້າຈົດຈໍາ" ເພື່ອເຊື່ອມຕໍ່ຊຸມຊົນ Web2 ແລະ Web3 

MemeCore dia manambara ny "Fetin'ny MemeX: Aoka Ianao, Aoka ho Tadidinao" mba hamehezana ny vondrom-piarahamonina Web2 sy Web3 

MemeCore kondigt “MemeX Festival: Be Yourself, Be Memorable” aan om Web2- en Web3-gemeenschappen te verbinden

MemeCore Web2 va Web3 hamjamiyatlarini bogʻlash uchun “MemeX festivali: Oʻzing boʻl, esda qolarli boʻl” deb eʼlon qiladi 

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References Limitations By enabling access to large language models, the GPT4All project also inherits many of the ethical concerns associated with generative models. Principal among these is the concern that unfiltered language models like GPT4All enable malicious users to generate content that could be harmful and dangerous (e.g., instructions on building bioweapons). While we recognize this risk, we also acknowledge the risk of concentrating this technology in the hands of a limited number of increasingly secretive research groups. We believe that the risk of focusing on the benefits of language model technology significantly outweighs the risk of misuse, and hence we prefer to make the technology as widely available as possible. Finally, we realize the challenge in assigning credit for large-scale open source initiatives. We make a first attempt at fair credit assignment by explicitly including the GPT4All open source developers as authors on this work, but recognize that this is insufficient fully characterize everyone involved in the GPT4All effort. Furthermore, we acknowledge the difficulty in citing open source works that do not necessarily have standardized citations, and do our best in this paper to provide URLs to projects whenever possible. We encourage further research in the area of open source credit assignment, and hope to be able to support some of this research ourselves in the future. References Nomic AI. 2023. Atlas. https://atlas.nomic.ai/. Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all. BBC News. 2023. Chatgpt banned in italy over privacy concerns. BBC News. Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling. Harrison Chase. 2022. langchain. https://github. com/langchain-ai/langchain. Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Ghodsi, Patrick Wendell, and Matei Zaharia. 2023a. Hello dolly: Democratizing the magic of chatgpt with open models. Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023b. Free dolly: Introducing the world’s first truly open instructiontuned llm. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. Koala: A dialogue model for academic research. Blog post. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. imartinez. 2023. privategpt. https://github.com/ imartinez/privateGPT. Oscar Leo. 2023. GitHub: The Fastest Growing Repositories of All Time. Robert McMillan. 2023. A meta platforms leak put powerful ai in the hands of everyone. The Wall Street Journal. MindsDB. 2023. Mindsdb. https://github.com/ mindsdb/mindsdb. GitHub repository. MosaicML-Team. 2023. Introducing mpt-7b: A new standard for open-source, commercially usable llms. Accessed: 2023-08-07. Nous-Research. 2023a. gpt4-x-vicuna-13b. https://huggingface.co/NousResearch/ gpt4-x-vicuna-13b. Model on Hugging Face. Nous-Research. 2023b. Nous-hermes-13b. https://huggingface.co/NousResearch/ Nous-Hermes-13b. Model on Hugging Face. Nous-Research. 2023c. Nous-hermes-llama-2-7b. https://huggingface.co/NousResearch/ Nous-Hermes-llama-2-7b. Model on Hugging Face. Nous-Research. 2023d. Redmond-puffin-13b. https://huggingface.co/NousResearch/ Redmond-Puffin-13B. Model on Hugging Face. OpenAI. 2023. Gpt-4 technical report. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021. Multitask prompted training enables zero-shot task generalization. Stability-AI. 2023. Stablelm. https://github.com/ Stability-AI/StableLM. GitHub repository. StanGirard. 2023. quivr. https://github.com/ StanGirard/quivr. GitHub repository. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https:// github.com/tatsu-lab/stanford_alpaca. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. The Verge. 2023. Meta’s powerful ai language model has leaked online — what happens now? The Verge. James Vincent. 2023. As an ai generated language model: The phrase that shows how ai is polluting the web. The Verge. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/ mesh-transformer-jax. Eric J. Wang. 2023. alpaca-lora. https://github. com/tloen/alpaca-lora. GitHub repository. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References Limitations By enabling access to large language models, the GPT4All project also inherits many of the ethical concerns associated with generative models. Principal among these is the concern that unfiltered language models like GPT4All enable malicious users to generate content that could be harmful and dangerous (e.g., instructions on building bioweapons). While we recognize this risk, we also acknowledge the risk of concentrating this technology in the hands of a limited number of increasingly secretive research groups. We believe that the risk of focusing on the benefits of language model technology significantly outweighs the risk of misuse, and hence we prefer to make the technology as widely available as possible. Finally, we realize the challenge in assigning credit for large-scale open source initiatives. We make a first attempt at fair credit assignment by explicitly including the GPT4All open source developers as authors on this work, but recognize that this is insufficient fully characterize everyone involved in the GPT4All effort. Furthermore, we acknowledge the difficulty in citing open source works that do not necessarily have standardized citations, and do our best in this paper to provide URLs to projects whenever possible. We encourage further research in the area of open source credit assignment, and hope to be able to support some of this research ourselves in the future. References Nomic AI. 2023. Atlas. https://atlas.nomic.ai/. Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. Yuvanesh Anand, Zach Nussbaum, Brandon Duderstadt, Benjamin Schmidt, and Andriy Mulyar. 2023. Gpt4all: Training an assistant-style chatbot with large scale data distillation from gpt-3.5-turbo. https://github.com/nomic-ai/gpt4all. BBC News. 2023. Chatgpt banned in italy over privacy concerns. BBC News. Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023. Pythia: A suite for analyzing large language models across training and scaling. Harrison Chase. 2022. langchain. https://github. com/langchain-ai/langchain. Mike Conover, Matt Hayes, Ankit Mathur, Xiangrui Meng, Jianwei Xie, Jun Wan, Ali Ghodsi, Patrick Wendell, and Matei Zaharia. 2023a. Hello dolly: Democratizing the magic of chatgpt with open models. Mike Conover, Matt Hayes, Ankit Mathur, Jianwei Xie, Jun Wan, Sam Shah, Ali Ghodsi, Patrick Wendell, Matei Zaharia, and Reynold Xin. 2023b. Free dolly: Introducing the world’s first truly open instructiontuned llm. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. Koala: A dialogue model for academic research. Blog post. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. imartinez. 2023. privategpt. https://github.com/ imartinez/privateGPT. Oscar Leo. 2023. GitHub: The Fastest Growing Repositories of All Time. Robert McMillan. 2023. A meta platforms leak put powerful ai in the hands of everyone. The Wall Street Journal. MindsDB. 2023. Mindsdb. https://github.com/ mindsdb/mindsdb. GitHub repository. MosaicML-Team. 2023. Introducing mpt-7b: A new standard for open-source, commercially usable llms. Accessed: 2023-08-07. Nous-Research. 2023a. gpt4-x-vicuna-13b. https://huggingface.co/NousResearch/ gpt4-x-vicuna-13b. Model on Hugging Face. Nous-Research. 2023b. Nous-hermes-13b. https://huggingface.co/NousResearch/ Nous-Hermes-13b. Model on Hugging Face. Nous-Research. 2023c. Nous-hermes-llama-2-7b. https://huggingface.co/NousResearch/ Nous-Hermes-llama-2-7b. Model on Hugging Face. Nous-Research. 2023d. Redmond-puffin-13b. https://huggingface.co/NousResearch/ Redmond-Puffin-13B. Model on Hugging Face. OpenAI. 2023. Gpt-4 technical report. Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush. 2021. Multitask prompted training enables zero-shot task generalization. Stability-AI. 2023. Stablelm. https://github.com/ Stability-AI/StableLM. GitHub repository. StanGirard. 2023. quivr. https://github.com/ StanGirard/quivr. GitHub repository. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. https:// github.com/tatsu-lab/stanford_alpaca. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models. The Verge. 2023. Meta’s powerful ai language model has leaked online — what happens now? The Verge. James Vincent. 2023. As an ai generated language model: The phrase that shows how ai is polluting the web. The Verge. Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/ mesh-transformer-jax. Eric J. Wang. 2023. alpaca-lora. https://github. com/tloen/alpaca-lora. GitHub repository. Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions. Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. 2023. Wizardlm: Empowering large language models to follow complex instructions. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

GPT4All: Limitations and References

(1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu);
(2) Bichen Wu, Meta GenAI and Corresponding author;
(3) Jialiang Wang, Meta GenAI;
(4) Licheng Yu, Meta GenAI;
(5) Kunpeng Li, Meta GenAI;
(6) Yinan Zhao, Meta GenAI;
(7) Ishan Misra, Meta GenAI;
(8) Jia-Bin Huang, Meta GenAI;
(9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com);
(10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com);
(11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction
2. Related Work
3. Preliminary
4. FlowVid
4.1. Inflating image U-Net to accommodate video
4.2. Training with joint spatial-temporal conditions
4.3. Generation: edit the first frame then propagate


Experiments


5.1. Settings
5.2. Qualitative results
5.3. Quantitative results
5.4. Ablation study and 5.5. Limitations
Conclusion, Acknowledgments and References
A. Webpage Demo and B. Quantitative comparisons 5.4. Ablation study Condition combinations We study the four types of conditions in Figure 6(a): (I) Spatial controls: such as depth maps [38]. (II) Flow warped video: frames warped from the first frame using optical flow. (III) Flow occlusion: masks indicate which parts are occluded (marked as white). (IV) First frame. We evaluate combinations of these conditions in Figure 6(b), assessing their effectiveness by their winning rate against our full model which contains all four conditions. The spatial-only condition achieved a 9% winning rate, limited by its lack of temporal information. Including flow warped video significantly improved the winning rate to 38%, underscoring the importance of temporal guidance. We use gray pixels to indicate occluded areas, which might blend in with the original gray colors in the images. To avoid potential confusion, we further include a binary flow occlusion mask, which better helps the model to tell which part is occluded or not. The winning rate is further improved to 42%. Finally, we added the first frame condition to provide better texture guidance, particularly useful when the occlusion mask is large and few original pixels remain. Different control type: edge and depth We study two types of spatial conditions in our FlowVid: canny edge [5] and depth map [38]. Given an input frame as shown in Figure 7(a), the canny edge retains more details than the depth map, as seen from the eyes and mouth of the panda. The strength of spatial control would, in turn, affect the video editing. For style transfer prompt ’A Chinese ink painting of a panda eating bamboo’, as shown in Figure 7(c), the output of canny condition could keep the mouth of the panda in the right position while the depth condition would guess where the mouth is and result in an open mouth. The flexibility of the depth map, however, would be beneficial if we are doing object swap with prompt ’A koala eating bamboo’, as shown in Figure 7(d); the canny edge would put a pair of panda eyes on the face of the koala due to the strong control, while depth map would result in a better koala edit. During our evaluation, we found canny edge works better when we want to keep the structure of the input video as much as possible, such as stylization. The depth map works better if we have a larger scene change, such as an object swap, which requires more considerable editing flexibility. v**-prediction and** ϵ**-prediction** While ϵ-prediction is commonly used for parameterization in diffusion models, we found it may suffer from unnatural global color shifts across frames, as shown in Figure 8. Even though all these two methods use the same flow warped video, the ϵ-prediction introduces an unnatural grayer color. This phenomenon is also found in Imagen-Video [17]. 5.5. Limitations Although our FlowVid achieves significant performance, it does have some limitations. First, our FlowVid heavily relies on the first frame generation, which should be structurally aligned with the input frame. As shown in Figure 9(a), the edited first frame identifies the hind legs of the elephant as the front nose. The erroneous nose would propagate to the following frame and result in an unsatisfactory final prediction. The other challenge is when the camera or the object moves so fast that large occlusions occur. In this case, our model would guess, sometimes hallucinate, the missing blank regions. As shown in Figure 9(b), when the ballerina turns her body and head, the entire body part is masked out. Our model manages to handle the clothes but turns the back of the head into the front face, which would be confusing if displayed in a video. This paper is available on arxiv under CC 4.0 license. (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). (1) Feng Liang, The University of Texas at Austin and Work partially done during an internship at Meta GenAI (Email: jeffliang@utexas.edu); (2) Bichen Wu, Meta GenAI and Corresponding author; (3) Jialiang Wang, Meta GenAI; (4) Licheng Yu, Meta GenAI; (5) Kunpeng Li, Meta GenAI; (6) Yinan Zhao, Meta GenAI; (7) Ishan Misra, Meta GenAI; (8) Jia-Bin Huang, Meta GenAI; (9) Peizhao Zhang, Meta GenAI (Email: stzpz@meta.com); (10) Peter Vajda, Meta GenAI (Email: vajdap@meta.com); (11) Diana Marculescu, The University of Texas at Austin (Email: dianam@utexas.edu). Table of Links Abstract and Introduction 2. Related Work 3. Preliminary 4. FlowVid 4.1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate Experiments 5.1. Settings 5.2. Qualitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons Abstract and Introduction Abstract and Introduction 2. Related Work 2. Related Work 3. Preliminary 3. Preliminary 4. FlowVid 4. FlowVid 4 .1. Inflating image U-Net to accommodate video .1. Inflating image U-Net to accommodate video 4.2. Training with joint spatial-temporal conditions 4.2. Training with joint spatial-temporal conditions 4.3. Generation: edit the first frame then propagate 4.3. Generation: edit the first frame then propagate Experiments Experiments Experiments 5.1. Settings 5.1. Settings 5.2. Qualitative results 5.2. Qualitative results 5.3. Quantitative results 5.3. Quantitative results 5.4. Ablation study and 5.5. Limitations 5.4. Ablation study and 5.5. Limitations Conclusion, Acknowledgments and References Conclusion, Acknowledgments and References A. Webpage Demo and B. Quantitative comparisons A. Webpage Demo and B. Quantitative comparisons 5.4. Ablation study Condition combinations We study the four types of conditions in Figure 6(a): (I) Spatial controls: such as depth maps [38]. (II) Flow warped video: frames warped from the first frame using optical flow. (III) Flow occlusion: masks indicate which parts are occluded (marked as white). (IV) First frame. We evaluate combinations of these conditions in Figure 6(b), assessing their effectiveness by their winning rate against our full model which contains all four conditions. The spatial-only condition achieved a 9% winning rate, limited by its lack of temporal information. Including flow warped video significantly improved the winning rate to 38%, underscoring the importance of temporal guidance. We use gray pixels to indicate occluded areas, which might blend in with the original gray colors in the images. To avoid potential confusion, we further include a binary flow occlusion mask, which better helps the model to tell which part is occluded or not. The winning rate is further improved to 42%. Finally, we added the first frame condition to provide better texture guidance, particularly useful when the occlusion mask is large and few original pixels remain. Condition combinations Different control type: edge and depth We study two types of spatial conditions in our FlowVid: canny edge [5] and depth map [38]. Given an input frame as shown in Figure 7(a), the canny edge retains more details than the depth map, as seen from the eyes and mouth of the panda. The strength of spatial control would, in turn, affect the video editing. For style transfer prompt ’A Chinese ink painting of a panda eating bamboo’, as shown in Figure 7(c), the output of canny condition could keep the mouth of the panda in the right position while the depth condition would guess where the mouth is and result in an open mouth. The flexibility of the depth map, however, would be beneficial if we are doing object swap with prompt ’A koala eating bamboo’, as shown in Figure 7(d); the canny edge would put a pair of panda eyes on the face of the koala due to the strong control, while depth map would result in a better koala edit. During our evaluation, we found canny edge works better when we want to keep the structure of the input video as much as possible, such as stylization. The depth map works better if we have a larger scene change, such as an object swap, which requires more considerable editing flexibility. Different control type: edge and depth v**-prediction and** ϵ**-prediction** While ϵ-prediction is commonly used for parameterization in diffusion models, we found it may suffer from unnatural global color shifts across frames, as shown in Figure 8. Even though all these two methods use the same flow warped video, the ϵ-prediction introduces an unnatural grayer color. This phenomenon is also found in Imagen-Video [17]. 5.5. Limitations Although our FlowVid achieves significant performance, it does have some limitations. First, our FlowVid heavily relies on the first frame generation, which should be structurally aligned with the input frame. As shown in Figure 9(a), the edited first frame identifies the hind legs of the elephant as the front nose. The erroneous nose would propagate to the following frame and result in an unsatisfactory final prediction. The other challenge is when the camera or the object moves so fast that large occlusions occur. In this case, our model would guess, sometimes hallucinate, the missing blank regions. As shown in Figure 9(b), when the ballerina turns her body and head, the entire body part is masked out. Our model manages to handle the clothes but turns the back of the head into the front face, which would be confusing if displayed in a video. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

FlowVid: Taming Imperfect Optical Flows: Ablation Study and Limitations

Orca 2: Enhancing Reasoning in Smaller Language Models - Conclusions and References

Authors:
(1) Arindam Mitra;
(2) Luciano Del Corro, work done while at Microsoft;
(3) Shweti Mahajan, work done while at Microsoft;
(4) Andres Codas, denote equal contributions;
(5) Clarisse Simoes, denote equal contributions;
(6) Sahaj Agarwal;
(7) Xuxi Chen, work done while at Microsoft;;
(8) Anastasia Razdaibiedina, work done while at Microsoft;
(9) Erik Jones, work done while at Microsoft;
(10) Kriti Aggarwal, work done while at Microsoft;
(11) Hamid Palangi;
(12) Guoqing Zheng;
(13) Corby Rosset;
(14) Hamed Khanpour;
(15) Ahmed Awadall. Table of Links Abstract and Introduction Preliminaries Teaching Orca 2 to be a Cautious Reasoner Technical Details Experimental Setup Evaluation Results Limitations Conclusions and References A. AGIEval Subtask Metrics B. BigBench-Hard Subtask Metrics C. Evaluation of Grounding in Abstractive Summarization D. Evaluation of Safety E. Prompts used in Evaluation F. Illustrative Example from Evaluation Benchmarks and Corresponding Model Outpu 2 Preliminaries 2.1 Instruction Tuning Instruction tuning [46, 38, 62, 61] has emerged as a crucial step in training language models. Instruction tuning involves learning from input-output pairs where the input is natural language task description,and the output is a demonstration of the desired behavior. Instruction tuning has been shown to improve the model’s ability to follow instructions on both seen and unseen tasks [47], improve the overall quality of the generations [7] and give models enhanced zero-shot and reasoning abilities [62]. Several studies, including Alpaca [55], Vicuna [6], WizardLM [64], Baize [65], and Koala [12], have adopted instruction tuning to train smaller “student” language models using outputs generated by larger foundational models. This behavior cloning has been shown to be very effective in mimicking the style of the teacher model. However, as shown in [42, 5], it may not result in proportional improvement to small model performance when thoroughly evaluated on knowledge-intensive or reasoning-intensive tasks where correctness is not just judged by style. We note that instruction tuning, while very beneficial for teaching the model how to solve a task, does not necessarily teach the model new knowledge. Hence instruction tuned models will be always limited by the knowledge learned during pre-training. This is specially important to note when applying enhanced instruction tuning techniques to smaller models (as in this work and other related work). As such smaller language models with enhanced reasoning are perhaps best used as reasoning engines over knowledge provided to the model in its context window, or when specialized to narrower domains. 2.2 Explanation Tuning One of the known weaknesses of instruction tuning is that a resulting student model could learn to generate stylistically correct, but ultimately wrong, outputs [13]. For example, instruction-tuning towards targets that are too terse limits the student’s visibility into what could have been a complex reasoning process, thus hindering its generalization ability to other tasks. In Orca 1, we introduced Explanation Tuning [42] to address this drawback by training student models on richer and more expressive reasoning signals. The mechanism for procuring these signals is system instructions[2] crafted to obtain detailed explanations from a teacher model as it reasons through a task. System instructions are additional high level guidelines an LLM is supposed to adhere to as it addresses individual user prompts, from which they are separated by a “system” role flag in a ChatML dialogue interface [3]. Explanation tuning begins with a compilation of N hand-crafted, general purpose system instructions designed to elicit more careful reasoning. Some examples include “think step-by-step”, “generate detailed answers”, etc. The primary objective of these system instructions is to extract rich demonstrations of “Slow Thinking” [22] from capable LLMs like GPT-4. They are then combined with user prompts from a vast and diverse set of tasks to yield a dataset of (system instruction, user prompt, LLM answer) triplets. The student model is trained to predict the LLM answer from the other two inputs. If user prompts can be grouped into M distinct clusters representing similar kinds of questions, then Explanation Tuning naively yields a cross product of M × N different answers addressing different aspects of the task. Since more capable LLMs tend to vary their responses with the system instruction, this offers an easy path to increase the quantity and diversity of training signals. Numerous models such as Orca 1 [42], StableBeluga [35] and Dolphin[4] have capitalized on Explanation Tuning to demonstrate substantial improvements over traditional instruction-tuned models, especially in complex zero-shot reasoning tasks. This paper is available on arxiv under CC 4.0 license. [2] used interchangeably with system message [3] e.g. see https://platform.openai.com/docs/api-reference/making-requests [4] https://huggingface.co/datasets/ehartford/dolphin Authors: (1) Arindam Mitra; (2) Luciano Del Corro, work done while at Microsoft; (3) Shweti Mahajan, work done while at Microsoft; (4) Andres Codas, denote equal contributions; (5) Clarisse Simoes, denote equal contributions; (6) Sahaj Agarwal; (7) Xuxi Chen, work done while at Microsoft;; (8) Anastasia Razdaibiedina, work done while at Microsoft; (9) Erik Jones, work done while at Microsoft; (10) Kriti Aggarwal, work done while at Microsoft; (11) Hamid Palangi; (12) Guoqing Zheng; (13) Corby Rosset; (14) Hamed Khanpour; (15) Ahmed Awadall. Authors: Authors: (1) Arindam Mitra; (2) Luciano Del Corro, work done while at Microsoft; (3) Shweti Mahajan, work done while at Microsoft; (4) Andres Codas, denote equal contributions; (5) Clarisse Simoes, denote equal contributions; (6) Sahaj Agarwal; (7) Xuxi Chen, work done while at Microsoft;; (8) Anastasia Razdaibiedina, work done while at Microsoft; (9) Erik Jones, work done while at Microsoft; (10) Kriti Aggarwal, work done while at Microsoft; (11) Hamid Palangi; (12) Guoqing Zheng; (13) Corby Rosset; (14) Hamed Khanpour; (15) Ahmed Awadall. Table of Links Abstract and Introduction Abstract and Introduction Preliminaries Preliminaries Teaching Orca 2 to be a Cautious Reasoner Teaching Orca 2 to be a Cautious Reasoner Technical Details Technical Details Experimental Setup Experimental Setup Evaluation Results Evaluation Results Limitations Limitations Conclusions and References Conclusions and References A. AGIEval Subtask Metrics A. AGIEval Subtask Metrics B. BigBench-Hard Subtask Metrics B. BigBench-Hard Subtask Metrics C. Evaluation of Grounding in Abstractive Summarization C. Evaluation of Grounding in Abstractive Summarization D. Evaluation of Safety D. Evaluation of Safety E. Prompts used in Evaluation E. Prompts used in Evaluation F. Illustrative Example from Evaluation Benchmarks and Corresponding Model Outpu F. Illustrative Example from Evaluation Benchmarks and Corresponding Model Outpu 2 Preliminaries 2.1 Instruction Tuning Instruction tuning [46, 38, 62, 61] has emerged as a crucial step in training language models. Instruction tuning involves learning from input-output pairs where the input is natural language task description,and the output is a demonstration of the desired behavior. Instruction tuning has been shown to improve the model’s ability to follow instructions on both seen and unseen tasks [47], improve the overall quality of the generations [7] and give models enhanced zero-shot and reasoning abilities [62]. Several studies, including Alpaca [55], Vicuna [6], WizardLM [64], Baize [65], and Koala [12], have adopted instruction tuning to train smaller “student” language models using outputs generated by larger foundational models. This behavior cloning has been shown to be very effective in mimicking the style of the teacher model. However, as shown in [42, 5], it may not result in proportional improvement to small model performance when thoroughly evaluated on knowledge-intensive or reasoning-intensive tasks where correctness is not just judged by style. We note that instruction tuning, while very beneficial for teaching the model how to solve a task, does not necessarily teach the model new knowledge. Hence instruction tuned models will be always limited by the knowledge learned during pre-training. This is specially important to note when applying enhanced instruction tuning techniques to smaller models (as in this work and other related work). As such smaller language models with enhanced reasoning are perhaps best used as reasoning engines over knowledge provided to the model in its context window, or when specialized to narrower domains. 2.2 Explanation Tuning One of the known weaknesses of instruction tuning is that a resulting student model could learn to generate stylistically correct, but ultimately wrong, outputs [13]. For example, instruction-tuning towards targets that are too terse limits the student’s visibility into what could have been a complex reasoning process, thus hindering its generalization ability to other tasks. In Orca 1, we introduced Explanation Tuning [42] to address this drawback by training student models on richer and more expressive reasoning signals. The mechanism for procuring these signals is system instructions[2] crafted to obtain detailed explanations from a teacher model as it reasons through a task. System instructions are additional high level guidelines an LLM is supposed to adhere to as it addresses individual user prompts, from which they are separated by a “system” role flag in a ChatML dialogue interface [3]. Explanation tuning begins with a compilation of N hand-crafted, general purpose system instructions designed to elicit more careful reasoning. Some examples include “think step-by-step”, “generate detailed answers”, etc. The primary objective of these system instructions is to extract rich demonstrations of “Slow Thinking” [22] from capable LLMs like GPT-4. They are then combined with user prompts from a vast and diverse set of tasks to yield a dataset of (system instruction, user prompt, LLM answer) triplets. The student model is trained to predict the LLM answer from the other two inputs. If user prompts can be grouped into M distinct clusters representing similar kinds of questions, then Explanation Tuning naively yields a cross product of M × N different answers addressing different aspects of the task. Since more capable LLMs tend to vary their responses with the system instruction, this offers an easy path to increase the quantity and diversity of training signals. Numerous models such as Orca 1 [42], StableBeluga [35] and Dolphin[4] have capitalized on Explanation Tuning to demonstrate substantial improvements over traditional instruction-tuned models, especially in complex zero-shot reasoning tasks. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [2] used interchangeably with system message [3] e.g. see https://platform.openai.com/docs/api-reference/making-requests [4] https://huggingface.co/datasets/ehartford/dolphin

Orca 2: Enhancing Reasoning in Smaller Language Models - Preliminaries

Multilingual Coarse Political Stance Classification of Media: Acknowledgments and References

মিডিয়ার বহুভাষিক মোটা রাজনৈতিক অবস্থানের শ্রেণীবিভাগ: স্বীকৃতি এবং উল্লেখ

Mehrsprachige grobe politische Haltung Klassifizierung von Medien: Danksagungen und Referenzen

Clasificación multilingüe de posturas políticas generales de los medios: reconocimientos y referencias

Classification multilingue grossière des positions politiques des médias : remerciements et références

बहुभाषी असभ्य राजनीतिक रुख मीडिया का वर्गीकरण: आभार और संदर्भ

多言語メディアの粗い政治的立場の分類: 謝辞と参考文献

미디어의 다국어 거친 정치적 입장 분류: 감사의 글 및 참고 자료

Classificação Multilíngue da Posição Política Grosseira da Mídia: Agradecimentos e Referências

Многоязычная грубая политическая классификация СМИ: благодарности и ссылки

Medyanın Çok Dilli Kaba Siyasi Duruş Sınıflandırması: Teşekkür ve Referanslar

Phân loại quan điểm chính trị thô sơ đa ngôn ngữ của phương tiện truyền thông: Lời cảm ơn và tài liệu tham khảo

多语言媒体粗略政治立场分类：致谢与参考文献

This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro
Corpora Compilation
Political Stance Classification
Summary and Conclusions
Limitations and Ethics Statement
Acknowledgments and References
A. Newspapers in OSCAR 22.01
B. Topics
C. Distribution of Topics per Newspaper
D. Subjects for the ChatGPT and Bard Article Generation
E. Stance Classification at Article Level
F. Training Details Abstract Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of the news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages. 1. Introduction Instruction-following language models (ILMs) are omnipresent. Their use is not so extended as that of search engines yet, but due to the availability and high quality of systems and models such as Alpaca (Taori et al., 2023), Bard (Google, 2023), BLOOMZ and mT0 (Muennighoff et al., 2023), ChatGPT (OpenAI, 2023), Llama 2-chat (Touvron et al., 2023), or Koala (Geng et al., 2023), their use is expected to be more common in the near future. These models face several problems being the most relevant the lack of trustworthiness (van Dis et al., 2023; Huang et al., 2023; Wang et al., 2023a). They are not ready to be used as a source of reliable information if their outputs are not fact-checked. A second big issue with systems based on language models (LM) is the fact that they might reproduce the biases present in the training data (Navigli et al., 2023). Biases range from cultural miss-representation due to data imbalance to offensive behaviour reproduced from written texts. LMs are finetuned into ILMs either in a supervised way using input-output pairs and an instruction (Wei et al., 2022; Wang et al., 2022, 2023b) or with reinforcement learning from human feedback (Ouyang et al., 2022; Nakano et al., 2021). In both cases, the finetuning should help removing bias. But neutrality is something very difficult to achieve, also for the humans that generate the supervisory data. The finetuning phase might therefore over correct the original biases or introduce new ones. For methods that generate the supervision data with the LM itself, the original biases might be inherited. We focus on a specific use of ILMs: the writing of newspaper articles. Journals and newspapers follow an editorial line which is in general known to the reader. Besides, sites such AllSides [1] Media Bias Fact Check [2] (MB/FC), or Ad Fontes Media [3] provide ratings about the political bias of (mostly USA) media sources and their quality with respect to factual information. With these ratings, conscientious readers can make informed decisions about which media outlets to choose in order to get a balanced perspective. But what happens when journalists use systems such as ChatGPT or Bard to aid in their writing? As said above, humans also have biases, the danger lies in being unaware of them, as they might affect the user’s/reader’s perspective (Jakesch et al., 2023; Carroll et al., 2023). ChatGPT already warns its users about misinformation. However, the political bias, if any, is not known apart from the subjective perception that a user has. We address the question above for articles generated by ChatGPT and Bard in four languages: English, German, Spanish and Catalan. We do this in an automatic and systematic way with almost no human intervention so that the method can be easily extended to new languages and other ILMs with few effort. We do not aim at classifying individual articles with their specific bias, but to classify the media source (an ILM in this case) as Left or Right-oriented in a similar way as the media bias sites do for newspapers and other media outlets. 1. https://www.allsides.com 2. https://mediabiasfactcheck.com 3. https://adfontesmedia.com This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: Authors: (1) Cristina España-Bonet, DFKI GmbH, Saarland Informatics Campus. Table of Links Abstract and Intro Corpora Compilation Political Stance Classification Summary and Conclusions Limitations and Ethics Statement Acknowledgments and References A. Newspapers in OSCAR 22.01 B. Topics C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level F. Training Details Abstract and Intro Abstract and Intro Corpora Compilation Corpora Compilation Political Stance Classification Political Stance Classification Summary and Conclusions Summary and Conclusions Limitations and Ethics Statement Limitations and Ethics Statement Acknowledgments and References Acknowledgments and References A. Newspapers in OSCAR 22.01 A. Newspapers in OSCAR 22.01 B. Topics B. Topics C. Distribution of Topics per Newspaper C. Distribution of Topics per Newspaper D. Subjects for the ChatGPT and Bard Article Generation D. Subjects for the ChatGPT and Bard Article Generation E. Stance Classification at Article Level E. Stance Classification at Article Level F. Training Details F. Training Details Abstract Neutrality is difficult to achieve and, in politics, subjective. Traditional media typically adopt an editorial line that can be used by their potential readers as an indicator of the media bias. Several platforms currently rate news outlets according to their political bias. The editorial line and the ratings help readers in gathering a balanced view of the news. But in the advent of instruction-following language models, tasks such as writing a newspaper article can be delegated to computers. Without imposing a biased persona, where would an AI-based news outlet lie within the bias ratings? In this work, we use the ratings of authentic news outlets to create a multilingual corpus of news with coarse stance annotations (Left and Right) along with automatically extracted topic annotations. We show that classifiers trained on this data are able to identify the editorial line of most unseen newspapers in English, German, Spanish and Catalan. We then apply the classifiers to 101 newspaper-like articles written by ChatGPT and Bard in the 4 languages at different time periods. We observe that, similarly to traditional newspapers, ChatGPT editorial line evolves with time and, being a data-driven system, the stance of the generated articles differs among languages. 1. Introduction Instruction-following language models (ILMs) are omnipresent. Their use is not so extended as that of search engines yet, but due to the availability and high quality of systems and models such as Alpaca (Taori et al., 2023), Bard (Google, 2023), BLOOMZ and mT0 (Muennighoff et al., 2023), ChatGPT (OpenAI, 2023), Llama 2-chat (Touvron et al., 2023), or Koala (Geng et al., 2023), their use is expected to be more common in the near future. These models face several problems being the most relevant the lack of trustworthiness (van Dis et al., 2023; Huang et al., 2023; Wang et al., 2023a). They are not ready to be used as a source of reliable information if their outputs are not fact-checked. A second big issue with systems based on language models (LM) is the fact that they might reproduce the biases present in the training data (Navigli et al., 2023). Biases range from cultural miss-representation due to data imbalance to offensive behaviour reproduced from written texts. LMs are finetuned into ILMs either in a supervised way using input-output pairs and an instruction (Wei et al., 2022; Wang et al., 2022, 2023b) or with reinforcement learning from human feedback (Ouyang et al., 2022; Nakano et al., 2021). In both cases, the finetuning should help removing bias. But neutrality is something very difficult to achieve, also for the humans that generate the supervisory data. The finetuning phase might therefore over correct the original biases or introduce new ones. For methods that generate the supervision data with the LM itself, the original biases might be inherited. We focus on a specific use of ILMs: the writing of newspaper articles. Journals and newspapers follow an editorial line which is in general known to the reader. Besides, sites such AllSides [1] Media Bias Fact Check [2] (MB/FC), or Ad Fontes Media [3] provide ratings about the political bias of (mostly USA) media sources and their quality with respect to factual information. With these ratings, conscientious readers can make informed decisions about which media outlets to choose in order to get a balanced perspective. But what happens when journalists use systems such as ChatGPT or Bard to aid in their writing? As said above, humans also have biases, the danger lies in being unaware of them, as they might affect the user’s/reader’s perspective (Jakesch et al., 2023; Carroll et al., 2023). ChatGPT already warns its users about misinformation. However, the political bias, if any, is not known apart from the subjective perception that a user has. We address the question above for articles generated by ChatGPT and Bard in four languages: English, German, Spanish and Catalan. We do this in an automatic and systematic way with almost no human intervention so that the method can be easily extended to new languages and other ILMs with few effort. We do not aim at classifying individual articles with their specific bias, but to classify the media source (an ILM in this case) as Left or Right-oriented in a similar way as the media bias sites do for newspapers and other media outlets. 1. https://www.allsides.com https://www.allsides.com 2. https://mediabiasfactcheck.com https://mediabiasfactcheck.com 3. https://adfontesmedia.com https://adfontesmedia.com

Multilingual Coarse Political Stance Classification of Media: Abstract and Intro

মিডিয়ার বহুভাষিক মোটা রাজনৈতিক অবস্থানের শ্রেণীবিভাগ: বিমূর্ত এবং ভূমিকা

Mehrsprachige grobe politische Standpunktklassifizierung von Medien: Zusammenfassung und Einführung

Clasificación multilingüe de posturas políticas generales de los medios: resumen e introducción

Classification multilingue grossière des positions politiques des médias : résumé et introduction

बहुभाषी असभ्य राजनीतिक रुख मीडिया का वर्गीकरण: सार और परिचय

多言語メディアの粗い政治的立場の分類：概要と序論

미디어의 다국어 거친 정치적 입장 분류: 개요 및 소개

Classificação Multilíngue da Posição Política Grosseira da Mídia: Resumo e Introdução

Многоязычная грубая политическая классификация СМИ: Аннотация и введение

Medyanın Çok Dilli Kaba Siyasi Duruş Sınıflandırması: Özet ve Giriş

Phân loại lập trường chính trị thô sơ đa ngôn ngữ của truyền thông: Tóm tắt và giới thiệu

多语言媒体粗略政治立场分类：摘要和简介

This paper is under CC 4.0 license. available on arxiv Authors: (1) Joel Jang, CarperAI,University of Washington & Allen Institute for AI; (2) Seungone Kim, KAIST AI; (3) Yizhong Wang, University of Washington; (4) Jack Hessel, University of Washington; (5) Luke Zettlemoyer, Aleph Alpha; (6) Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; (7) Yejin Choi, UC San Diego. 5 CONCLUSION Previous work has shown that adapting LLMs with RLHF helps them generate outputs that are preferred by humans over the supervised fine-tuned counterpart. However, recent work has also pointed out that simply training LLMs to abide by the preference of the general may result in ignoring individual preferences and values. In this work, we provide the first steps to tackle this issue by proposing Reinforcement Learning from Personalized Human Feedback as a multi-objective problem so that LLMs can be aligned to follow conflicting preferences. We propose a promising method called P-SOUPS that is able to composite models trained on single objectives on the fly during inference. We also highlight the scalability of P-SOUPS by showing that it scales linearly, instead of exponentially like the MORL baseline, with regards to the number of new preferences, which is required to provide true personalization to individual users. ACKNOWLEDGMENTS Thanks to Minyoung Hwang, Sungdong Kim, Tim Dettmers, Yoonjoo Lee, and Margaret Li for helpful feedback and discussion. REFERENCES Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, and Xing Xie. PENS: A dataset and generic framework for personalized news headline generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 82–92, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long.7. 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Judging llm-as-a-judge with mt-bench and chatbot arena. arXiv preprint arXiv:2306.05685, 2023. Yinhe Zheng, Guanyi Chen, Minlie Huang, Song Liu, and Xuan Zhu. Personalized dialogue generation with diversified traits. arXiv preprint arXiv:1901.09672, 2019. Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593, 2019. A DETAILS OF EVALUATION SETUP We use a modified version of the GPT4 annotation prompt used by Dubois et al. (2023). We modify the criteria to perform the pairwise evaluation from general to a single preference dimension. We also provide 3 demonstrations: one scenario where there is a tie because both responses do not contain any notion of the preference (e.g. both responses do not show any signs of friendliness), one scenario where there is a clear winner, and one scenario where they are both good, but one is better than the other. Simulated pairwise evaluation We recruited 24 crowd workers for our human evaluation. Figure 5 shows the interface used for human evaluation. We consider both the ‘Tie’ and ‘Both are bad’ options to be Ties. B CRITERIA-WISE EVALUATION The criteria-wise win rate (%) across all the methods are shown in Figure 6. The criteria-wise win rate was calculated by getting the average win-rate of the preference combinations that contained the specific preference dimension. For example, when calculating the criteria-wise win rate of ‘Elementary’, we got the average win-rate of the preference combinations that contained the ‘Elementary’ preference, which includes AAA, AAB, ABA, and ABB. C THE FULL LIST OF EVALUATION PROMPTS The full list of evaluation prompts used in our experiments are provided in Table 5 D DETAILED RESULTS FOR HUMAN EVALUATION AND GPT-4 EVALUATION We provide detailed results (win / loss / tie for each of the preference combinations for our main experimental results. Table 6 shows the GPT4 evaluation and Table 7 shows the human evaluation results. E EXAMPLES OF P-SOUPS TEXT GENERATIONS Table 8 shows empirical examples of the text generated from each preference combination of the 16 preference combination experiments for the same prompt.

Personalized Soups: LLM Alignment Via Parameter Merging - Conclusion & References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Joel Jang, CarperAI,University of Washington & Allen Institute for AI; (2) Seungone Kim, KAIST AI; (3) Yizhong Wang, University of Washington; (4) Jack Hessel, University of Washington; (5) Luke Zettlemoyer, Aleph Alpha; (6) Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; (7) Yejin Choi, UC San Diego. 3 REINFORCEMENT LEARNING FROM Personalized HUMAN FEEDBACK Collecting Conflicting Pairwise Feedback We utilize Tulu-7B LM (Wang et al., 2023), a model that uses LLaMA-7B (Touvron et al., 2023) as a base model and is instruction tuned on a mixture of open-source instruction-tuning datasets, as the base model for our experiments. We utilize 10k prompt instances from GPT4-Alpaca (Peng et al., 2023), one of the datasets used to train Tulu-7B, as our instruction dataset Dtrain to generate rollouts and collect pairwise feedback data. We also use the same Dtrain during Proximal Policy Optimization (PPO) training (Schulman et al., 2017) of Tulu-7B. Following previous work, we simulate human annotators with GPT-4 for collecting large-scale pairwise feedback data (Bai et al., 2022b; Dubois et al., 2023)—but note that our evaluations are validated with (smaller-scale) human preference data collected from crowdworkers. While Dubois et al. (2023) mostly simulates GPT-4 and other LLMs to choose which is generally a better response between two candidate responses, we provide GPT-4 with a single preference (full list shown in Table 1) to decide which is a better response. We also provide the same preference criteria via additional prompts during the rollout generation of the two candidate responses; we use Tulu-30B for the rollout generation while the actual policy model we train is Tulu-7B for our main experimental setup, making our experimental setting an off-policy training set-up. While we have feedback on which of the two model responses is more aligned with a single preference via GPT-4 annotation, utilizing only two positive pairs during reward model training was empirically shown to be less robust during the PPO training. Instead, we train our reward model on multiple comparisons (Song et al., 2023; Kim et al., 2023) by including a neutral response and a negative response as shown in Figure 2. Specifically, the reward model is provided with four different comparisons for a single prompt during training: positive 1 > positive 2 (decided by GPT-4), positive > neutral, positive > negative, and neutral > negative. The positive response when compared with the neutral and the negative response is chosen randomly. This allows the reward model to be exposed to different granularity of the specific preference and give scores accordingly during PPO training. We explore (1) training a single reward model in a multitask fashion that leverages the preference prompts during inference to give distinct rewards according to each preference and (2) training multiple reward models, each tailored to the distinct preference. Reward Model Training 3.2 MULTI-OBJECTIVE REINFORCEMENT LEARNING (MORL) The MORL problem can be denoted as: 3.3 MULTIFACETED EVALUATION For evaluation, we manually filter out 50 instances from the Koala evaluation (Geng et al., 2023) that require open-ended generations. We also modified some of the prompts so that the evaluation prompts do not contain any elements requiring individual preferences (e.g., removing the phrase asking for a elementary-level response from the original prompt since we want to test the LLM to generate a expert-level response). The full list of evaluation prompts used for our experiments is shown in Appendix C. In our evaluation setup, we simulate users to have a unique combination of preferences, each from the three preference dimensions (Expertise, Informativeness, Style) in Table 1, which equates to 8 unique preference combinations (examples shown in Figure 1). We get the average win rate across the simulated 8 preference combinations for our final evaluation. We use a variant of the AlpacaFarm evaluation framework for simulated (GPT4) evaluation and hire 24 crowdworkers for human evaluation. Details of human evaluation are provided in Appendix A. Evaluation [3] P1, P2, P3 each represents preference prompts from each preference dimension in Table 1. For one example, one unique combination might be P1A + P2B + P3A (ABA) where the combined objective for the response needs to be elementary level, informative, and friendly. [4] Empirically, utilizing a single reward model instead of multiple reward models led to better performance. We hypothesize this is due to the problem of normalizing signals from different reward models (Hayes et al., 2022), which is known to be a nontrivial problem in MORL.

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