Table of Links
3 Methodology and 3.1 Causal language model as a classification model
3.4 Model development and training
4 Experiments and 4.1 Android function calls
4.2 Extension to Vehicle, Yelp, and DoorDash function sets
4.3 Full and partial training datasets and 4.4 Full training and LoRA training
4.5 Parallel and nested function call and 4.6 Weighted loss function for special tokens
5 Discussion and future works and References
Appendix
4.2 Extension to Vehicle, Yelp, and DoorDash function sets
In addition to Android function calls, we expanded our evaluation to include 20 vehicle function calls, showcasing the algorithm’s adaptability to diverse use cases. For vehicle functions, we focused
on essential control methods such as volume adjustment, air conditioning, and seat positioning. We conducted benchmarks for vehicle functions paralleling the Android function evaluation, observing consistent performance patterns. Details on vehicle functions are provided in the Appendix, enabling users to customize a new set of functional APIs for their specific needs. Furthermore, tests conducted with Yelp and DoorDash APIs confirmed a similar performance, underscoring our method’s versatility across various function sets.
This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.
Authors:
(1) Wei Chen, Stanford University, with equal contribution and a corresponding author {weichen6}@stanford.edu;
(2) Zhiyuan Li, Stanford University and a corresponding author {zhiyuan8}@stanford.edu.
