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cs.CL2025
ReviewInstruct: A Review-Driven Multi-Turn Conversations Generation Method for Large Language Models
Jiangxu Wu, Cong Wang, TianHuang Su +10
The effectiveness of large language models (LLMs) in conversational AI is hindered by their reliance on single-turn supervised fine-tuning (SFT) data, which limits contextual coher…
cs.CL2025
ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding
Zhongxiang Sun, Qipeng Wang, Weijie Yu +6
Retrieval-Augmented Generation (RAG) systems for Large Language Models (LLMs) hold promise in knowledge-intensive tasks but face limitations in complex multi-step reasoning. While…