9 papers
UR: Unify RAG and Reasoning through Reinforcement Learning
Weitao Li, Boran Xiang, Xiaolong Wang +3
Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Lear…
Enhancing LLM Metacognition via Cognitive Pairwise Training
Weitao Li, Hao Zhou, Xuanyu Lei +11
Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to give confident answers when…
From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions
Xiaolong Wang, Zhe Zhao, Song Lai +5
While LLMs show promise in automating educational content creation, their ability to generate questions that stimulate higher-order thinking remains understudied. This work evaluat…
Beyond "I Don't Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty
Jingyi Ren, Ante Wang, Yunghwei Lai +5
Reliable Large Language Models (LLMs) should abstain when confidence is insufficient. However, prior studies often treat refusal as a generic "I don't know'', failing to distinguis…
Enabling Stroke-Level Structural Analysis of Hieroglyphic Scripts without Language-Specific Priors
Fuwen Luo, Zihao Wan, Ziyue Wang +6
Hieroglyphs, as logographic writing systems, encode rich semantic and cultural information within their internal structural composition. Yet, current advanced Large Language Models…
Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learning
Jingyi Ren, Yekun Xu, Xiaolong Wang +4
Retrieval-Augmented Generation (RAG) delivers substantial value in knowledge-intensive applications. However, its generated responses often lack transparent reasoning paths that tr…