Papers by Chaoyi Chen

2 papers
PUPPET: Neural-Symbolic Standardized Patients for Mental Health (2026.acl-long)

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Challenge: Existing LLM-based training approaches lack faithful responses to clinical errors and explainable feedback.
Approach: They propose a neural-symbolic virtual standardized patient governed by an OBSERVE-THINK-BEHAVE architecture that embeds LLM reasoning into a symbolic system where experts implant causal associations between intervention logic and patient mental states.
Outcome: The proposed model outperforms baselines in faithfulness and pedagogical value.
Duwak: Dual Watermarks in Large Language Models (2024.findings-acl)

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Challenge: Existing watermark techniques are effective in embedding single human-imperceptible and machine-detectable patterns without significantly affecting generated text quality and semantics.
Approach: They propose to embed dual secret patterns in token probability distribution and sampling schemes to enhance the efficiency of watermarking.
Outcome: The proposed method achieves highest watermark quality at the lowest required token count for detection, up to 70% less than existing techniques, especially under post paraphrasing attacks.

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