Papers by Lillian Sun
Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models (2026.acl-long)
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| Challenge: | Recent studies have highlighted weak-to-strong generalization, where a strong model trained only on a weak model’s labels surpasses the weak model in task performance. |
| Approach: | They propose two fundamental fine-tuning strategies that leverage trustworthiness regularization during the fine-uning of the weak model and the weak-to-strong transfer to improve trustworthy. |
| Outcome: | The proposed models show that they can generalize robustness, fairness, and privacy better when trained on weak models than models trained on strong models. |