Papers by Hy Dang
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
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Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
| Approach: | They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls. |
| Outcome: | The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents. |
Optimizing Decomposition for Optimal Claim Verification (2025.acl-long)
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| Challenge: | Existing decomposition and verification paradigms ignore their interactions and potential misalignment. |
| Approach: | They propose a reinforcement learning framework that leverages verifier feedback to learn a policy for dynamically decomposing claims to verifier-preferred atomicity. |
| Outcome: | The proposed framework outperforms existing decomposition policies in verification confidence tests . it improves accuracy and confidence by 0.12 on average across varying verifiers, datasets, and atomcities of input claims. |
OZSpeech: One-step Zero-shot Speech Synthesis with Learned-Prior-Conditioned Flow Matching (2025.acl-long)
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| Challenge: | Text-to-speech systems have seen significant advances in recent years, driven by improvements in deep learning and neural network architectures. |
| Approach: | They propose a method to explore optimal transport conditional flow matching with one-step sampling and a learned prior as the condition, effectively disregarding preceding states and reducing the number of sampling steps. |
| Outcome: | The proposed method achieves promising performance over existing methods in content accuracy, naturalness, prosody generation, and speaker style preservation. |