What question did this study set out to answer?

To present the TMUNLPG3 team's approaches for lung cancer staging and multi-label sentence classification in the NTCIR-18 RadNLP Task.

April 1, 2026Open Access

TMUNLPG3 at the NTCIR-18 RadNLP Task

Key Points

To present the TMUNLPG3 team's approaches for lung cancer staging and multi-label sentence classification in the NTCIR-18 RadNLP Task.
Participated in Lung Cancer TNM Staging main task and Multi-label Sentence Classification subtask
Employed large language models (LLM) and few-shot prompt engineering
Analyzed challenges and official results of the tasks
Achieved top performance in Lung Cancer TNM Staging among all participants in the English track
Excelled in the Multi-label Sentence Classification subtask

Abstract

The TMUNLPG3 team participated in the Lung Cancer Staging main task and Multi-label Sentence Classification subtask of the NTCIR-18 RadNLP Task. This paper illustrates our approach to address the challenges and discusses the official results. We tackled Lung Cancer TNM Staging maintask to highest among all participants in the English track by adopting LLM and Few-Shot prompt engineering. Our solution also performed excellently in the Multi-label Sentence Classification subtask.

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Cite This Study

Yeh et al. (Fri,) studied this question.

synapsesocial.com/papers/69cd7ac55652765b073a83de https://doi.org/https://doi.org/10.20736/0002002073

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