Papers by Kahee Lim
DeFrame: Debiasing Large Language Models Against Framing Effects (2026.findings-acl)
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| Challenge: | Existing debiasing methods improve overall fairness, but fail to reduce framing-induced disparities. |
| Approach: | They propose a framing-aware debiasing method that encourages LLMs to be more consistent across frams. |
| Outcome: | The proposed method reduces overall bias and improves robustness against framing disparities, enabling LLMs to produce fairer and more consistent responses. |