通过多智能体辩论提升语言模型的事实性与推理能力
Improving Factuality and Reasoning in Language Models through Multiagent Debate
AI总结：
提出多智能体辩论方法，让多个语言模型实例多轮辩论以达成共识，显著提升了数学与战略推理能力及事实有效性，减少幻觉，可直接应用于黑盒模型。
摘要：
近年来，大型语言模型（LLMs）在语言生成、理解和少样本学习中展现出了卓越的能力。大量研究探索了如何通过提示工具进一步提升其性能，包括验证、自洽性或中间草稿板。在本文中，我们提出了一种互补方法来改进语言响应，其中多个语言模型实例提出并就各自的响应和推理过程进行多轮辩论，以达成共同的最终答案。我们的研究结果表明，这种方法在多项任务中显著增强了数学和战略推理能力。我们还证明，该方法提高了生成内容的事实有效性，减少了当代模型容易产生的错误答案和幻觉。该方法可直接应用于现有的黑盒模型，并在我们研究的所有任务中使用相同的流程和提示。总体而言，我们的研究结果表明，这种“心智社会”方法有望显著提升LLMs的能力，并为语言生成和理解领域的进一步突破铺平道路。
英文摘要：
Large language models (LLMs) have demonstrated remarkable capabilities in language generation, understanding, and few-shot learning in recent years. An extensive body of work has explored how their performance may be further improved through the tools of prompting, ranging from verification, self-consistency, or intermediate scratchpads. In this paper, we present a complementary approach to improve language responses where multiple language model instances propose and debate their individual responses and reasoning processes over multiple rounds to arrive at a common final answer. Our findings indicate that this approach significantly enhances mathematical and strategic reasoning across a number of tasks. We also demonstrate that our approach improves the factual validity of generated content, reducing fallacious answers and hallucinations that contemporary models are prone to. Our approach may be directly applied to existing black-box models and uses identical procedure and prompts for all tasks we investigate. Overall, our findings suggest that such "society of minds" approach has the potential to significantly advance the capabilities of LLMs and pave the way for further breakthroughs in language generation and understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
