2 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
TRiMS: Real-Time Tracking of Minimal Sufficient Length for Efficient Reasoning via RL
Tingcheng Bian, Jinchang Luo, Mingquan Cheng +5
Large language models achieve breakthroughs in complex reasoning via long chain-of-thought sequences. However, this often leads to severe reasoning inflation, causing substantial c…
GlobalRAG: Enhancing Global Reasoning in Multi-hop Question Answering via Reinforcement Learning
Jinchang Luo, Mingquan Cheng, Fan Wan +7
Reinforcement learning has recently shown promise in improving retrieval-augmented generation (RAG). Despite these advances, its effectiveness in multi-hop question answering (QA)…
Select to Know: An Internal-External Knowledge Self-Selection Framework for Domain-Specific Question Answering
Bolei He, Xinran He, Run Shao +5
Large Language Models (LLMs) perform well in general QA but often struggle in domain-specific scenarios. Retrieval-Augmented Generation (RAG) introduces external knowledge but suff…
MHPP: Exploring the Capabilities and Limitations of Language Models Beyond Basic Code Generation
Jianbo Dai, Jianqiao Lu, Yunlong Feng +6
Recent advancements in large language models (LLMs) have greatly improved code generation, specifically at the function level. For instance, GPT-4o has achieved a 91.0\% pass rate…