Papers by Jingyuan Deng
Robust and Minimally Invasive Watermarking for EaaS (2025.findings-acl)
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| Challenge: | Existing watermarking methods use a target embedding to create watermarks, but this method results in each embeddable having the same component, making it difficult to remove the watermark. |
| Approach: | They propose to use embedding watermarks to protect EaaS from model extraction attacks . eaas is vulnerable to model extraction, highlighting the need for copyright protection . |
| Outcome: | The proposed method can watermark embeddings against model extraction attacks without sacrificing the quality of the embeddables. |
Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances MCQ Generation and Distractor Quality (2025.findings-acl)
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| Challenge: | Existing methods for generating high-quality MCQs struggle with contextual relevance and plausible distractors. |
| Approach: | They propose a framework that integrates bidirectional reasoning perspectives to generate contextually relevant questions and plausible distractors while student reasoning evaluates question clarity and the misleading nature of distractors. |
| Outcome: | The proposed framework outperforms existing methods in generating text-grounded questions and high-quality distractors for narrative contexts. |
CFlowPsyD: An Analysis-Enhanced Dataset for Asynchronous Psychological Counseling through Self-Optimizing Multi-Agent Framework (2026.findings-acl)
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| Challenge: | Asynchronous psychological counseling (APC) is a crucial mental health service modality that transcends temporal and spatial constraints. |
| Approach: | They propose a self-optimizing multi-agent framework for counseling dialogue generation, CFlowPsy, which utilizes real anonymized counseling cases as seed data to synthesize diverse problem-solving-oriented APC conversations through large language models. |
| Outcome: | The proposed framework synthesizes diverse problem-solving-oriented APC conversations through large language models. |
MaskCD: Mitigating LVLM Hallucinations by Image Head Masked Contrastive Decoding (2025.findings-emnlp)
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| Challenge: | LVLMs have shown remarkable performance in visual-language understanding for downstream multimodal tasks. |
| Approach: | They propose a method to alleviate hallucinations by masking the “image heads” in LVLMs . |
| Outcome: | The proposed method alleviates the phenomenon of hallucinations and retains the general capabilities of LVLMs. |