3 papers
cs.CL2024
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Kun Zhu, Xiaocheng Feng, Xiyuan Du +7
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when con…
cs.CL2024
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering
Zheng Chu, Jingchang Chen, Qianglong Chen +6
Large language models (LLMs) have demonstrated strong reasoning capabilities. Nevertheless, they still suffer from factual errors when tackling knowledge-intensive tasks. Retrieval…
cs.CL2023
Learning to Break: Knowledge-Enhanced Reasoning in Multi-Agent Debate System
Haotian Wang, Xiyuan Du, Weijiang Yu +5
Multi-agent debate system (MAD) imitating the process of human discussion in pursuit of truth, aims to align the correct cognition of different agents for the optimal solution. It…