Papers by Seunghee Koh
Forget What Matters, Keep the Rest: Selective Unlearning of Informative Tokens (2026.acl-long)
Copied to clipboard
| 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. |