Participants

@Tina Tsou @Wilson Wang @Borui Li (李博睿)   @Tom Qin @Victor Lu @Caleb Vivian @Moshe Shadmon Dev

Meeting Summary

@Wilson Wang

https://bytedance.us.larkoffice.com/minutes/obusask4b84z836vsg7g813l

The meeting discussed the application of large language models in hypothesis search and inductive reasoning, and its potential use cases in edge computing and other fields, the main contents included:

• Inductive Reasoning and Hypothesis Search: Inductive reasoning involves logical thinking to generalize from specific evidence, and hypothesis search is a crucial algorithm in machine learning.

• Use Cases in Edge Computing and Cyber Security: Hypothesis search can be combined with edge computing for a more efficient private and intelligent distributed system, and it is also important in cyber security for threat detection.

• Datasets and Evaluation: The authors used the ARC dataset to evaluate the performance of large language models in generating hypotheses and implementing them as Python programs.

• Study Findings and Limitations: The best performance was observed with human-written hypotheses, but there were limitations in visual pattern recognition and the size of the grid.

• Next Steps and Potential Applications: Further exploration is needed to find a suitable benchmark for evaluating large language models, and there is potential for applications in edge computing and other areas.