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April 1, 20260 citationsOpen Access

SOCIOCOM at the NTCIR-18 RadNLP Main task: Zero-Shot LLM Approaches for Lung Cancer Staging

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YTYuki TashiroYNYuta NAKAMURAEAEiji Aramaki

Key Points

  • The aim is to classify lung cancer stages from radiology reports using advanced language models.
  • Utilized GPT-4o model for inference.
  • Employed prompt engineering techniques to enhance model performance.
  • Participated in the NTCIR-18 RadNLP 2024 main task for evaluation.
  • Achieved an accuracy of 0.5648 on Japanese test data.
  • Demonstrated robustness of the closed-source models in classification tasks.

Abstract

This paper describes our approach to the RadNLP 2024 Maintask as participants of NTCIR-18. The RADNLP 2024 Main Task is to classify the stage of lung cancer from radiology reports. Our approach utilizes GPT-4o for inference, employing prompt engineering techniques. We achieved an accuracy of 0.5648 on the Japanese test data, demonstrating the robustness of closed-source models.

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Cite This Study

Tashiro et al. (2025) studied this question.

synapsesocial.com/papers/69cd7ad45652765b073a85c4https://doi.org/10.20736/0002002075
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