对比医生与大模型对乳腺癌免疫治疗标志物的识别能力
ImmunoFOMO: Are Language Models missing what oncologists see?
- 用临床专家标注数据测试不同语言模型对免疫治疗特征的识别能力
- 预训练模型在识别低层级具体概念上优于大型语言模型
- 适合医学NLP研究者关注模型在专业任务中的表现差异
过去十年间，语言模型（LMs）的能力迅速提升，促使生物医学等领域的研究者探索其在日常应用中的潜力。领域特定语言模型已广泛用于生物医学自然语言处理（NLP）。近期，人们对医学语言模型及其理解能力的兴趣显著增长。本文通过对比专家临床医生，研究了多种语言模型在乳腺癌摘要中识别免疫治疗标志性特征的医学概念基础。结果表明，预训练语言模型在识别非常具体的（低层级）概念方面具有超越大型语言模型的潜力。
原文摘要 · Abstract (English)
Language models (LMs) capabilities have grown with a fast pace over the past decade leading researchers in various disciplines, such as biomedical research, to increasingly explore the utility of LMs in their day-to-day applications. Domain specific language models have already been in use for biomedical natural language processing (NLP) applications. Recently however, the interest has grown towards medical language models and their understanding capabilities. In this paper, we investigate the medical conceptual grounding of various language models against expert clinicians for identification of hallmarks of immunotherapy in breast cancer abstracts. Our results show that pre-trained language models have potential to outperform large language models in identifying very specific (low-level) concepts.
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