Papers by Woody Haosheng Gan
AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation (2026.eacl-long)
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Potsawee Manakul, Woody Haosheng Gan, Michael J Ryan, Ali Sartaz Khan, Warit Sirichotedumrong, Kunat Pipatanakul, William Barr Held, Diyi Yang
| Challenge: | Current speech evaluation systems rely on specialized systems for individual audio characteristics and poor correlation between automatic methods and human preferences. |
| Approach: | They propose a unified evaluation framework for Large Audio Models as a Judge, AudioJudge . they propose specialized judges that can be prompted to perform audio characteristic detection tasks . |
| Outcome: | The proposed method improves performance across audio characteristic detection and human preference simulation tasks. |
Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment (2026.acl-long)
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| Challenge: | Existing LAM benchmarks with thousands of examples create substantial computational barriers. |
| Approach: | They examine whether subsets can reliably evaluate large audio models . they find that subset of 50 examples can achieve over 0.93 Pearson correlation with full benchmark . |
| Outcome: | The proposed method outperforms the full benchmark and subset selection methods. |
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models (2026.acl-long)
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Woody Haosheng Gan, Deqing Fu, Julian Asilis, Ollie Liu, Vatsal Sharan, Robin Jia, Willie Neiswanger
| Challenge: | Steering methods have emerged as effective tools for guiding large language models’ behavior, yet multimodal large language model lacks comparable techniques due to architectural diversity and limited availability of multimodal steering vectors. |
| Approach: | They validate steering vectors derived solely from text-only LLM backbones and use a cross-modal transfer technique to reuse existing interpretability tools. |
| Outcome: | The proposed steering vectors can guide and enhance multimodal models using SPAR, Mean Shift, and Linear Probing. |