Tool-MAD: 一种用于事实验证的多智能体辩论框架，具有多样化工具增强和自适应检索
Tool-MAD: A Multi-Agent Debate Framework for Fact Verification with Diverse Tool Augmentation and Adaptive Retrieval
AI总结：
Tool-MAD通过多智能体辩论框架结合多样化工具增强和自适应检索，提升事实验证的准确性和鲁棒性。
摘要：
大型语言模型（LLMs）在复杂推理和事实验证任务中容易出现幻觉和事实不准确的问题。多智能体辩论（MAD）系统旨在通过让多个LLM代理进行对话来提高回答的准确性，促进多样化的推理和相互验证。然而，现有的MAD框架主要依赖内部知识或静态文档，使其容易产生幻觉。虽然MADKE引入了外部证据来缓解这一问题，但其一次性检索机制限制了在辩论过程中对新论点或新兴信息的适应性。为了解决这些限制，我们提出了Tool-MAD，一种多智能体辩论框架，通过为每个代理分配一个独特的外部工具（如搜索API或RAG模块）来增强事实验证。Tool-MAD引入了三个关键创新：（1）一个多智能体辩论框架，其中代理利用异质外部工具，鼓励多样化的观点；（2）一个自适应查询形成机制，根据辩论的流程迭代优化证据检索；（3）将忠实度和回答相关性评分整合到最终决策过程中，使法官代理能够定量评估每个响应的连贯性和问题对齐性，并有效检测幻觉。在四个事实验证基准上的实验结果表明，Tool-MAD在多个方面优于最先进的MAD框架，实现了高达5.5%的准确率提升。此外，在医学专业领域，Tool-MAD在各种工具配置和领域条件下表现出强大的鲁棒性和适应性，确认了其在更广泛现实事实核查应用中的潜力。
英文摘要：
Large Language Models (LLMs) suffer from hallucinations and factual inaccuracies, especially in complex reasoning and fact verification tasks. Multi-Agent Debate (MAD) systems aim to improve answer accuracy by enabling multiple LLM agents to engage in dialogue, promoting diverse reasoning and mutual verification. However, existing MAD frameworks primarily rely on internal knowledge or static documents, making them vulnerable to hallucinations. While MADKE introduces external evidence to mitigate this, its one-time retrieval mechanism limits adaptability to new arguments or emerging information during the debate. To address these limitations, We propose Tool-MAD, a multi-agent debate framework that enhances factual verification by assigning each agent a distinct external tool, such as a search API or RAG module. Tool-MAD introduces three key innovations: (1) a multi-agent debate framework where agents leverage heterogeneous external tools, encouraging diverse perspectives, (2) an adaptive query formulation mechanism that iteratively refines evidence retrieval based on the flow of the debate, and (3) the integration of Faithfulness and Answer Relevance scores into the final decision process, allowing the Judge agent to quantitatively assess the coherence and question alignment of each response and effectively detect hallucinations. Experimental results on four fact verification benchmarks demonstrate that Tool-MAD consistently outperforms state-of-the-art MAD frameworks, achieving up to 5.5% accuracy improvement. Furthermore, in medically specialized domains, Tool-MAD exhibits strong robustness and adaptability across various tool configurations and domain conditions, confirming its potential for broader real-world fact-checking applications.
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