Papers by Kenza Benkirane
Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models (2024.findings-emnlp)
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Kenza Benkirane, Laura Gongas, Shahar Pelles, Naomi Fuchs, Joshua Darmon, Pontus Stenetorp, David Adelani, Eduardo Sánchez
| Challenge: | Existing methods for detecting hallucinations in machine translation are limited for low-resource languages. |
| Approach: | They evaluate sentence-level hallucination detection approaches using Large Language Models (LLMs) they find that the choice of model is essential for performance. |
| Outcome: | The proposed models outperform the existing models in HRLs and LRLs on average by 0.16 MCC. |
How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making? (2025.naacl-long)
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| Challenge: | Recent studies have shown that LLMs exhibit social biases inherited from training data. |
| Approach: | They propose a framework for evaluation and mitigation of bias in Large Language Models applied to complex clinical cases using a dataset based on the JAMA Clinical Challenge. |
| Outcome: | The proposed framework employs multiple choice questions and explanations to evaluate gender and ethnicity biases in LLMs. |