Papers by Yanhao Wang
From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset (2026.acl-long)
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| Challenge: | Applied Behavior Analysis (ABA) is the gold standard for clinical intervention, but large language models struggle to adhere to its standardized procedures. |
| Approach: | They propose a strategy-aware framework to unify high-fidelity intervention dialogue synthesis and clinical decision support. |
| Outcome: | Experiments show that ASDAgent achieves nearly 80% strategic consistency with human experts. |
Think Faster Than Words: Efficient LLM Chain-of-Thought Reasoning via Dynamic Shortcut Decoding (2026.acl-long)
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| Challenge: | Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. |
| Approach: | They propose a shortcut decoding framework that integrates probes over internal hidden states with step-level entropy to detect convergence of reasoning during generation and adaptively selects between a fast-exit path and a stability-verified path to remove redundant steps while preserving answer correctness. |
| Outcome: | The proposed framework reduces token usage by approximately 35% and maintains accuracy comparable to full CoT decoding. |
A Customized Text Sanitization Mechanism with Differential Privacy (2023.findings-acl)
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| Challenge: | Existing methods to sanitize texts subject to differential privacy do not work for non-metric semantic similarity measures. |
| Approach: | They propose a customized text sanitization mechanism based on a metric local differential privacy definition. |
| Outcome: | The proposed mechanism achieves better privacy-utility trade-offs than existing mechanisms on benchmark datasets. |
TaiChi: Improving the Robustness of NLP Models by Seeking Common Ground While Reserving Differences (2024.lrec-main)
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| Challenge: | Pre-trained Language Models are vulnerable to adversarial examples that introduce human-imperceptible perturbations to clean examples to deceive the models. |
| Approach: | They propose a Siamese network-based approach to teach adversarial models to focus on similarities . they propose combining two sub-networks sharing the same structure but trained on clean and adversarials . |
| Outcome: | The proposed approach reduces the differences between clean and adversarial samples and focuses more on similarities. |