在高风险的监管研究中,LLM是否应该被赋予过多的权力?
1Department of Computer Applications, Sikkim University, Gangtok 737102, Sikkim, India.
Briefings in bioinformatics
|June 27, 2025
概括
开源的大型语言模型对监管研究有希望,但面临挑战. 本文探讨了在药物监管中使用这些人工智能工具的可行性和缓解策略.
科学领域:
- 药理学 药理学 是一个学科.
- 人工智能的人工智能
- 监管科学 监管科学
背景情况:
- 最近的研究探讨了使用大型语言模型 (LLM) 进行监管任务的零射击和少射击学习.
- 像Flan-T5这样的模型在提取FDA药物标签中的药物相互作用和内在因素方面表现出有效性.
- 然而,发现了一些重大实施挑战.
研究的目的:
- 批判性地评估在监管研究中使用开源大型语言模型 (LLM) 的可行性.
- 讨论针对已识别的LLM实施挑战的潜在缓解策略.
主要方法:
- 在监管任务中对LLM进行现有研究的批判性评估.
- 分析诸如计算约束,性能变化,提示敏感性和错误分类风险等挑战.
- 讨论用于解决这些局限性的直观方法.
主要成果:
- 像Flan-T5这样的开源LLM可以在从监管文件中提取特定数据 (例如药理学药物相互作用) 时实现高精度.
- 关键的挑战包括计算需求,不一致的性能,对输入提示的敏感性和错误的可能性.
结论:
- 在监管研究中实施开源LLM是可行的,但需要仔细考虑局限性.
- 开发直观的策略对于克服与LLM在这个领域的部署相关的计算,性能和准确性挑战至关重要.
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