Papers by Sihan Zhao

5 papers
A Computational Method for Measuring Open Codes in Qualitative Analysis (2026.findings-acl)

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Challenge: Qualitative analysis is widely adopted across many social science disciplines.
Approach: They propose a theory-informed computational method for measuring inductive coding results from humans and GAI.
Outcome: The proposed method captures breadth, consensus, unique contribution, and systematic deviation without assuming ground truth.
Improving Alignment in LVLMs with Debiased Self-Judgment (2025.findings-emnlp)

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Challenge: Existing methods for aligning LVLMs rely on external datasets, human annotations or complex post-processing.
Approach: They propose a method that generates a debiased self-judgment score for LVLMs . this self-evaluation metric is created internally by the model without external resources .
Outcome: The proposed approach outperforms existing methods in reducing hallucinations and safety concerns.
A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit (2025.findings-acl)

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Challenge: Large language models (LLMs) are being used to provide automated talk therapy . however, it is crucial to know if they would be effective and adhere to known standards.
Approach: They propose to use large language models to automate talk therapy with a focus on tobacco addiction.
Outcome: The proposed chatbot showed adherence to MI standards in 98% of utterances, higher than human counsellors.
CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models (2024.findings-emnlp)

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Challenge: Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration.
Approach: They propose a dataset to evaluate LLMs' ability to handle ambiguity in Chinese by using a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference.
Outcome: The CHAmbi dataset comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities.
Thinking Traps in Long Chain-of-Thought: A Measurable Study and Trap-Aware Adaptive Restart (2026.findings-acl)

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Challenge: Experiments show that extended generation does not guarantee correctness . a recurring pattern in Long-CoT failures is a problem for large reasoning models .
Approach: They propose a test-time control framework that truncates the trajectory before the trap segment and adaptively restarts decoding.
Outcome: Experiments show that TAAR improves reasoning performance without fine-tuning model parameters.

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