Papers by Yibin Lou
Purging the Gray Zone: Latent-Geometric Denoising for Precise Knowledge Boundary Awareness (2026.findings-acl)
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| Challenge: | Existing abstention fine-tuning methods cause models to suffer from label noise near the decision boundaries. |
| Approach: | They propose a latent space representation perspective for abstention fine-tuning . they propose 'geometric denoising' framework that constructs a truth hyperplane . |
| Outcome: | The proposed framework significantly enhances model truthfulness and demonstrates strong generalization in out-of-distribution scenarios. |