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,

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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

(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. URL https: //aclanthology.org/2021.acl-long.7. Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, T. J. Henighan, Nicholas Joseph, Saurav Kadavath, John Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom B. Brown, Jack Clark, Sam McCandlish, Christopher Olah, Benjamin Mann, and Jared Kaplan. Training a helpful and harmless assistant with reinforcement learning from human feedback. ArXiv, abs/2204.05862, 2022a. URL https://api.semanticscholar.org/ CorpusID:248118878. Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022b. Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jer´ emy Scheurer, Javier ´ Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, et al. Open problems and fundamental limitations of reinforcement learning from human feedback. arXiv preprint arXiv:2307.15217, 2023. Deborah Cohen, Moonkyung Ryu, Yinlam Chow, Orgad Keller, Ido Greenberg, Avinatan Hassidim, Michael Fink, Yossi Matias, Idan Szpektor, Craig Boutilier, et al. Dynamic planning in openended dialogue using reinforcement learning. arXiv preprint arXiv:2208.02294, 2022. Shachar Don-Yehiya, Elad Venezian, Colin Raffel, Noam Slonim, Yoav Katz, and Leshem Choshen. Cold fusion: Collaborative descent for distributed multitask finetuning. arXiv preprint arXiv:2212.01378, 2022. Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. Alpacafarm: A simulation framework for methods that learn from human feedback. arXiv preprint arXiv:2305.14387, 2023. Shangbin Feng, Chan Young Park, Yuhan Liu, and Yulia Tsvetkov. From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair nlp models. arXiv preprint arXiv:2305.08283, 2023. Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wallace, Pieter Abbeel, Sergey Levine, and Dawn Song. Koala: A dialogue model for academic research. Blog post, April, 1, 2023. Conor F Hayes, Roxana Radulescu, Eugenio Bargiacchi, Johan K ˘ allstr ¨ om, Matthew Macfarlane, ¨ Mathieu Reymond, Timothy Verstraeten, Luisa M Zintgraf, Richard Dazeley, Fredrik Heintz, et al. A practical guide to multi-objective reinforcement learning and planning. Autonomous Agents and Multi-Agent Systems, 36(1):26, 2022. Edward J Hu, yelong shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum? id=nZeVKeeFYf9. Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, and Min Lin. Lorahub: Efficient cross-task generalization via dynamic lora composition. ArXiv, abs/2307.13269, 2023. URL https://api.semanticscholar.org/CorpusID:260155012. Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, and Ali Farhadi. Editing models with task arithmetic. ICLR, 2022. Joel Jang, Seungone Kim, Seonghyeon Ye, Doyoung Kim, Lajanugen Logeswaran, Moontae Lee, Kyungjae Lee, and Minjoon Seo. Exploring the benefits of training expert language models over instruction tuning. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.), Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 14702–14729. PMLR, 23–29 Jul 2023. URL https://proceedings.mlr.press/v202/jang23a. html. Sungdong Kim, Sanghwan Bae, Jamin Shin, Soyoung Kang, Donghyun Kwak, Kang Min Yoo, and Minjoon Seo. Aligning large language models through synthetic feedback. arXiv preprint arXiv:2305.13735, 2023. Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, and Scott A Hale. Personalisation within bounds: A ¨ risk taxonomy and policy framework for the alignment of large language models with personalised feedback. arXiv preprint arXiv:2303.05453, 2023. Noriyuki Kojima, Alane Suhr, and Yoav Artzi. Continual learning for grounded instruction generation by observing human following behavior. Transactions of the Association for Computational Linguistics, 9:1303–1319, 2021. Margaret Li, Suchin Gururangan, Tim Dettmers, Mike Lewis, Tim Althoff, Noah A Smith, and Luke Zettlemoyer. Branch-train-merge: Embarrassingly parallel training of expert language models. arXiv preprint arXiv:2208.03306, 2022. Pan Li and Alexander Tuzhilin. Towards controllable and personalized review generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3237–3245, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 10.18653/v1/D19-1319. URL https://aclanthology.org/D19-1319. Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, and Julian McAuley. Generating personalized recipes from historical user preferences. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 5976–5982, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 10.18653/v1/D19-1613. URL https://aclanthology.org/D19-1613. Pierre-Emmanuel Mazare, Samuel Humeau, Martin Raison, and Antoine Bordes. Training mil- ´ lions of personalized dialogue agents. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2775–2779, Brussels, Belgium, OctoberNovember 2018. Association for Computational Linguistics. doi: 10.18653/v1/D18-1298. URL https://aclanthology.org/D18-1298. Jacob Menick, Maja Trebacz, Vladimir Mikulik, John Aslanides, Francis Song, Martin Chadwick, Mia Glaese, Susannah Young, Lucy Campbell-Gillingham, Geoffrey Irving, et al. Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147, 2022. Reiichiro Nakano, Jacob Hilton, S. Arun Balaji, Jeff Wu, Ouyang Long, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. Webgpt: Browser-assisted question-answering with human feedback. ArXiv, abs/2112.09332, 2021a. URL https://api.semanticscholar.org/CorpusID:245329531. Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332, 2021b. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe. Training language models to follow instructions with human feedback. ArXiv, abs/2203.02155, 2022a. URL https://api.semanticscholar.org/ CorpusID:246426909. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35: 27730–27744, 2022b. Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, and Jianfeng Gao. Instruction tuning with gpt-4. arXiv preprint arXiv:2304.03277, 2023. Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, Xian Li, James Cross, Sebastian Riedel, and Mikel Artetxe. Lifting the curse of multilinguality by pre-training modular transformers. arXiv preprint arXiv:2205.06266, 2022. Alexandre Rame, Guillaume Couairon, Mustafa Shukor, Corentin Dancette, Jean-Baptiste Gaya, Laure Soulier, and Matthieu Cord. Rewarded soups: towards pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards. arXiv preprint arXiv:2306.04488, 2023. Alireza Salemi, Sheshera Mysore, Michael Bendersky, and Hamed Zamani. Lamp: When large language models meet personalization. arXiv preprint arXiv:2304.11406, 2023. Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. Whose opinions do language models reflect? arXiv preprint arXiv:2303.17548, 2023. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. Umer Siddique, Paul Weng, and Matthieu Zimmer. Learning fair policies in multi-objective (deep) reinforcement learning with average and discounted rewards. In International Conference on Machine Learning, pp. 8905–8915. PMLR, 2020. David Silver, Satinder Singh, Doina Precup, and Richard S Sutton. Reward is enough. Artificial Intelligence, 299:103535, 2021. Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett. A long way to go: Investigating length correlations in rlhf. arXiv preprint arXiv:2310.03716, 2023. Feifan Song, Bowen Yu, Minghao Li, Haiyang Yu, Fei Huang, Yongbin Li, and Houfeng Wang. Preference ranking optimization for human alignment. arXiv preprint arXiv:2306.17492, 2023. Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008–3021, 2020. Alane Suhr and Yoav Artzi. Continual learning for instruction following from realtime feedback. arXiv preprint arXiv:2212.09710, 2022. Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018. Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239, 2022. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothee´ Lacroix, Baptiste Roziere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and ` efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin, and Jane Mummery. Humanaligned artificial intelligence is a multiobjective problem. Ethics and Information Technology, 20:27–40, 2018. Kristof Van Moffaert and Ann Nowe. Multi-objective reinforcement learning using sets of pareto ´ dominating policies. The Journal of Machine Learning Research, 15(1):3483–3512, 2014. Kristof Van Moffaert, Madalina M Drugan, and Ann Nowe. Scalarized multi-objective reinforce- ´ ment learning: Novel design techniques. In 2013 IEEE symposium on adaptive dynamic programming and reinforcement learning (ADPRL), pp. 191–199. IEEE, 2013. Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A Smith, Iz Beltagy, et al. How far can camels go? exploring the state of instruction tuning on open resources. arXiv preprint arXiv:2306.04751, 2023. Mitchell Wortsman, Suchin Gururangan, Shen Li, Ali Farhadi, Ludwig Schmidt, Michael Rabbat, and Ari S Morcos. lo-fi: distributed fine-tuning without communication. arXiv preprint arXiv:2210.11948, 2022a. Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International Conference on Machine Learning, pp. 23965–23998. PMLR, 2022b. Jeff Wu, Long Ouyang, Daniel M Ziegler, Nisan Stiennon, Ryan Lowe, Jan Leike, and Paul Christiano. Recursively summarizing books with human feedback. arXiv preprint arXiv:2109.10862, 2021a. Yuwei Wu, Xuezhe Ma, and Diyi Yang. Personalized response generation via generative split memory network. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1956–1970, 2021b. Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. Fine-grained human feedback gives better rewards for language model training. arXiv preprint arXiv:2306.01693, 2023. Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, and Wojciech Matusik. Prediction-guided multi-objective reinforcement learning for continuous robot control. In International conference on machine learning, pp. 10607–10616. PMLR, 2020. Jing Xu, Arthur Szlam, and Jason Weston. Beyond goldfish memory: Long-term open-domain conversation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5180–5197, Dublin, Ireland, May 2022. Association for Computational Linguistics. doi: 10.18653/v1/2022.acl-long.356. URL https: //aclanthology.org/2022.acl-long.356. Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan. A generalized algorithm for multi-objective reinforcement learning and policy adaptation. Advances in neural information processing systems, 32, 2019. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. Personalizing dialogue agents: I have a dog, do you have pets too? arXiv preprint arXiv:1801.07243, 2018. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. 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.

Personalized Soups: LLM Alignment Via Parameter Merging - Personalized Human Feedback

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