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cs.CL2024
Importance Weighting Can Help Large Language Models Self-Improve
Chunyang Jiang, Chi-min Chan, Wei Xue +2
Large language models (LLMs) have shown remarkable capability in numerous tasks and applications. However, fine-tuning LLMs using high-quality datasets under external supervision r…
cs.CL2024
AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems
Chi-Min Chan, Jianxuan Yu, Weize Chen +6
The rapid advancement of large language models (LLMs) has led to the rise of LLM-based agents. Recent research shows that multi-agent systems (MAS), where each agent plays a specif…
cs.CL2024
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
Chi-Min Chan, Chunpu Xu, Ruibin Yuan +4
Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pr…