4 papers
Domain-Aware RAG: MoL-Enhanced RL for Efficient Training and Scalable Retrieval
Hao Lin, Peitong Xie, Jingxue Chen +3
Retrieval-Augmented Generation (RAG) systems rely heavily on the retrieval stage, particularly the coarse-ranking process. Existing coarse-ranking optimization approaches often str…
MoL-RL: Distilling Multi-Step Environmental Feedback into LLMs for Feedback-Independent Reasoning
Kang Yang, Jingxue Chen, Qingkun Tang +2
Large language models (LLMs) face significant challenges in effectively leveraging sequential environmental feedback (EF) signals, such as natural language evaluations, for feedbac…
MoL for LLMs: Dual-Loss Optimization to Enhance Domain Expertise While Preserving General Capabilities
Jingxue Chen, Qingkun Tang, Qianchun Lu +1
Although large language models (LLMs) perform well in general tasks, domain-specific applications suffer from hallucinations and accuracy limitations. Continual Pre-Training (CPT)…
KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation
Siyuan Fang, Kaijing Ma, Tianyu Zheng +4
Large language models (LLMs) demonstrate exceptional performance across a variety of tasks, yet they are often affected by hallucinations and the timeliness of knowledge. Leveragin…