ATILF at NTCIR-18 RadNLP 2024 Shared Task: With less radiology reports, comes less performance (Poster)
Résumé
We present our results on the main task and subtask of the NTCIR-18 RadNLP 2024 shared task on the English language. We tested to what extent Large Language Models (LLMs) and Pretrained Language Models (PLMs) can identify and classify tumor types and subtypes. Our results for the main task showed that LLMs have difficulties in understanding different subtypes of tumors. For the tumor sentence segment classification subtask, we obtained competitive overall score with pretrained language models with an overall score of 0.83 for micro F2.0 metric. Our results showed that in low amount of data setting, we have a better chance with clinical PLMs in comparison to general and domain specific LLMs. Providing additional information such definitions in case clinical staging classification can help LLMs achieve better scores on fine-grained classification.
| Origine | Fichiers produits par l'(les) auteur(s) |
|---|---|
| Licence |