Papers by Kangwook Jang
Generalizable Prompt Tuning for Audio-Language Models via Semantic Expansion (2026.findings-acl)
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| Challenge: | Prompt tuning has achieved remarkable progress in vision–language models, but its generalization ability in ALMs remains underexplored. |
| Approach: | They propose a plug-and-play framework that regularizes the prompt embedding space . they propose introducing a semantic expansion loss with margin constraints that promote compactness . |
| Outcome: | The proposed framework regularizes the prompt embedding space by incorporating semantic neighbors generated by large language models. |
Two Heads Are Better Than One: Audio-Visual Speech Error Correction with Dual Hypotheses (2026.findings-acl)
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| Challenge: | Recent advances have introduced GER frameworks that utilize LLMs to refine ASR outputs. |
| Approach: | They propose a framework that allows a large language model to compose independent N-best hypotheses from separate automatic speech recognition (ASR) and visual speech recognition models. |
| Outcome: | The proposed framework achieves 57.7% error rate gain over standard ASR baseline, compared to single-stream approaches that achieve only 10% gain. |