Papers by Sunghyun Baek
How Do Large Vision-Language Models See Text in Image? Unveiling the Distinctive Role of OCR Heads (2025.emnlp-main)
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| Challenge: | Despite advances in Large Vision Language Models, a gap remains in their interpretability and performance. |
| Approach: | They identify the Optical Character Recognition Head (OCR Head) heads that are more efficient at recognizing text from images. |
| Outcome: | The Optical Character Recognition Head (OCR Head) is identified as the most efficient head for recognizing text from images. |
Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens (2026.acl-long)
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| Challenge: | Recent studies have explored token-wise loss regularizers that prioritize informative tokens, but rely on ground-truth confidence or external linguistic parsers, which limits their ability to capture contextual information or the model’s overall predictive state. |
| Approach: | They propose an Entropy-guided Token Weighting (ETW) token-level unlearning regularizer that uses entropy of the predictive distribution as a proxy for token informativeness. |
| Outcome: | The proposed token-level unlearning regularizer can achieve more effective unlearning while better preserving model utility than existing token-based approaches. |