DebateGPT: Fine-tuning Large Language Models with Multi-agent Debate Supervision

Vighnesh Subramaniam, Antonio Torralba, Shuang Li

Primary Area: generative models

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Keywords: Large Language Models, Fine-tuning, Factuality and Reasoning

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Abstract: In this paper, we introduce DebateGPT, a large language model (LLM), which achieves remarkable performance on language generation, comprehension, and reasoning without heavy reliance on resource-intensive human-in-the-loop feedback. DebateGPT is crafted by fine-tuning GPT-3.5 with a limited set of instructions extracted from Alpaca through a novel approach called multi-agent debate, achieving comparable performance with GPT-4 in various tasks. We leverage multi-agent debate, harnessing less robust but cost-effective LLMs to generate data without human annotations. Surprisingly, after fine-tuning GPT-3.5 on a modest-size Alpaca dataset obtained by multi-agent debate, DebateGPT shows similar results as GPT-4 on the AlpacaEval test set and showcases remarkable zero-shot generalization to new tasks like commonsense reasoning, factuality and mathematics. For example, DebateGPT outperforms GPT-4 by 2.2\% on the arithmetic task. Notably, DebateGPT is much smaller than GPT-4 and only uses a modest dataset. DebateGPT offers an innovative strategy for training highly effective language models without the need for expensive human-in-the-loop feedback or excessively large architectures.

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Submission Number: 4835