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cs.CL2026
Meta-Moderator: Empowering Multi-Agent Debate with Meta-Cognition
Wentao Hu, Zhuoyue Wan, Jinhao Shen +3
Multi-agent debate can improve large language model reasoning by eliciting diverse hypotheses and critiques, yet its performance is often constrained by weak moderation. Common pip…
cs.CL2025
Removal of Hallucination on Hallucination: Debate-Augmented RAG
Wentao Hu, Wengyu Zhang, Yiyang Jiang +3
Retrieval-Augmented Generation (RAG) enhances factual accuracy by integrating external knowledge, yet it introduces a critical issue: erroneous or biased retrieval can mislead gene…
cs.CL2025
Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models
Haoyang Li, Xuejia Chen, Zhanchao XU +8
Large Language Models (LLMs) have demonstrated impressive capabilities in natural language processing tasks, such as text generation and semantic understanding. However, their perf…