Papers by Zihang Li
RFS-Guard: Detecting Reasoning Hallucinations via Cross-Phase Routing Focus in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate intermediate reasoning traces before the final answer, yet they remain vulnerable to reasoning hallucinations such as subtle arithmetic errors. |
| Approach: | They propose a Routing Focus Score (RFS) that measures how strongly cross-step attention routing aligns with semantic proximity derived from hidden-state cosine similarity. |
| Outcome: | The proposed framework detects and localizes hallucinations without external tools or repeated sampling. |
Don’t Change Me! User-Controllable Selective Paraphrase Generation (2021.eacl-main)
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| Challenge: | a new technique allows paraphrase generation to be user-controlled . a user looking for cheap hotels in New York would not find the other answer helpful . |
| Approach: | They propose a method that provides a user with explicit tags that can be placed around any arbitrary segment of text to mean "don't change me!" they propose allowing user-controllable paraphrase generation by fine-tuning model that exhibits this behavior . |
| Outcome: | The proposed technique is language agnostic and tested in English and Chinese. |
Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use (2025.naacl-long)
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Mohit Chandra, Siddharth Sriraman, Gaurav Verma, Harneet Singh Khanuja, Jose Suarez Campayo, Zihang Li, Michael L. Birnbaum, Munmun De Choudhury
| Challenge: | Adverse Drug Reactions (ADRs) from psychiatric medications are the leading cause of hospitalizations among mental health patients. |
| Approach: | They propose a benchmark and a framework to evaluate LLMs' ability to detect ADRs . they find that LLM responses are more complex and harder to read than experts . |
| Outcome: | The proposed framework evaluates LLMs' ability to detect and deliver expert-aligned mitigation strategies. |
Do Large Language Models Align with Core Mental Health Counseling Competencies? (2025.findings-naacl)
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Viet Cuong Nguyen, Mohammad Taher, Dongwan Hong, Vinicius Konkolics Possobom, Vibha Thirunellayi Gopalakrishnan, Ekta Raj, Zihang Li, Heather J. Soled, Michael L. Birnbaum, Srijan Kumar, Munmun De Choudhury
| Challenge: | Large language models are promising for mental health, but their alignment with core counseling competencies remains underexplored. |
| Approach: | They propose a benchmark to evaluate 22 general-purpose and medical-finetuned LLMs across five key competencies. |
| Outcome: | The proposed model outperforms generalist models in Intake, Assessment & Diagnosis but struggles with core counseling attributes and professional practice & ethics. |
Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation (2026.acl-long)
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| Challenge: | X (formerly Twitter) users can flag misleading posts, attach contextual notes, and rate the notes’ helpfulness, but there is a significant latency in Community Notes, which is unable to provide accurate notes. |
| Approach: | They propose a framework that augments Community Notes for faster and more reliable health misinformation governance. |
| Outcome: | The proposed framework outperforms human contributors in correctness, helpfulness, and evidence utility in health misinformation surges. |