jennyzzt/LLM_debate_on_ARC
LLM Debate on ARC dataset
This project explores how a 'debate' among Large Language Models (LLMs) impacts their ability to solve abstract reasoning tasks, specifically those found in the ARC dataset. It takes a problem definition, often represented as input/output matrices, and uses either a direct answer from an LLM or code generated by an LLM. The output is an assessment of the LLM's performance based on how well its solution matches the correct answer. This is useful for researchers and practitioners working with advanced AI, particularly those focused on improving model reasoning and problem-solving.
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Use this if you are researching advanced AI capabilities and want to understand how 'multi-agent debate' can influence a large language model's performance on complex, pattern-based reasoning problems like those in the ARC dataset.
Not ideal if you need a plug-and-play solution for general LLM fine-tuning or immediate deployment in a business application, as this is a research-focused exploration of reasoning techniques.
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Language
Python
License
MIT
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Last pushed
Jun 15, 2024
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