Papers by Alexandre Perez-Lebel
Reconfidencing LLMs from the Grouping Loss Perspective (2024.findings-emnlp)
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| Challenge: | Existing methods to calibrate confidence scores for large language models often overlook biases towards certain groups, such as specific nationalities. |
| Approach: | They propose a method to calibrate confidence scores of Large Language Models by considering different groups, a process they call reconfidencing. |
| Outcome: | The proposed method mitigates biases against minority groups, the authors show . they show that the proposed method is more reliable than existing methods . |