21 citations · 24 across the 12 of their papers we have counts for
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cs.CL2026
When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation
Haolin Jin, Pengyue Yang, Huaming Chen
Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trust problem: retrieved context m…
cs.SE2026
Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement
Haolin Jin, Huaming Chen
Large language models (LLMs) have become essential tools in software development, widely used for requirements engineering, code generation and review tasks. Software engineers oft…
cs.CL2026
Trust in One Round: Confidence Estimation for Large Language Models via Structural Signals
Pengyue Yang, Jiawen Wen, Haolin Jin +3
Large language models (LLMs) are increasingly deployed in domains where errors carry high social, scientific, or safety costs. Yet standard confidence estimators, such as token lik…