most citedERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

195 citations · 198 across the 13 of their papers we have counts for

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cs.CL2026

Characterizing Treatment-Context Medication Evidence Across Clinic Notes and Structured EHR Medication History

Mingyang Jiang, Congning Ni, Weixin Liu +1

Clinic notes and structured electronic health record (EHR) medication history often contain different medication information. Same-visit disagreement between these sources may resu…

cs.CL2026

CoRA: Confidence-Rationale Alignment for Reliable Chain-of-Thought Reasoning

Juming Xiong, Weixin Liu, Kevin Guo +9

Chain-of-thought (CoT) reasoning can improve LLM performance, but high answer confidence may be misleading when the accompanying CoT rationale is plausible yet incomplete or poorly…

cs.CL2026

RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation

Weixin Liu, Juming Xiong, Yang Li +5

Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertain…

cs.CL2026

Vectors Are Not Neutral: Sensitive-Information Inference from Exported LLM Representations in Summarization

Weixin Liu, Bowen Qu, Juming Xiong +3

Large language model (LLM) summarization systems may pass compact vector representations of private inputs to downstream retrieval, monitoring, audit, or analytic workflows. Even w…

cs.CL2026

MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models

Weixin Liu, Congning Ni, Shelagh A. Mulvaney +4

Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledge and how reliably they appl…

cs.CL2026

Coverage-Controlled Preference Mining from Noisy Claim Verification for Evidence-Grounded Generation

Weixin Liu, Congning Ni, Qingyuan Song +4

Evidence-grounded generation produces summaries whose claims should be supported by supplied evidence, but claim-level verifiers provide noisy feedback and can reward models that s…