Adaptive Retrieval in Tool-MAD for Enhanced Robustness Against Adversarial Evidence in Fact Verification

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

Research goal: To what extent does the Tool-MAD framework's adaptive retrieval mechanism improve robustness against adversarial evidence compared to static retrieval methods on fact verification benchmarks?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.