Papers by Achir Oukelmoun
Detecting Omissions in LLM-Generated Medical Summaries (2025.emnlp-industry)
Copied to clipboard
Achir Oukelmoun, Nasredine Semmar, Gaël de Chalendar, Clement Cormi, Mariame Oukelmoun, Eric Vibert, Marc-Antoine Allard
| Challenge: | Large Language Models (LLMs) have created a number of use cases in the medical field . omissions in summaries can jeopardize the decision-making process . |
| Approach: | They propose a dataset to evaluate omissions in large-scale medical summaries . they propose 'embedKDECheck' method that uses embeddings generated by a third-party NLP model . |
| Outcome: | The proposed method is well-suited for resource-constrained environments. |