学习解释是一个很好的生物医学少数枪支学习者学习
Peng Chen1, Jian Wang1, Ling Luo1
1School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
Bioinformatics (Oxford, England)
|October 3, 2024
概括
产生解释可以提高生物医学的学习效率. 这种新的方法在低资源场景中改进了归纳推理,优于现有的模型.
科学领域:
- 生物医学自然语言处理 (NLP)
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 生物医学文本挖掘的深度学习需要广泛的专家注释的数据,这些数据往往稀缺,昂贵或对隐私敏感.
- 现有的方法主要专注于预测而没有提供解释,限制了它们在现实世界中的适用性.
- 由于数据的局限性,生物医学短暂学习是一个现实的挑战,需要能够从最小的数据有效地学习的方法.
研究的目的:
- 调查可解释性对生物医学短暂学习的影响.
- 开发一种新的方法,以增强低资源生物医学NLP任务中的感应推理.
- 通过利用大语言模型 (LLM) 的解释来应对数据稀缺的挑战.
主要方法:
- 介绍了LetEx-Learning,一种利用LLM生成的推理解释的多任务生成方法.
- 开发了一个使用思维链 (CoT) 提示和自我训练收集高质量的解释的工作流.
- 将各种生物医学NLP任务统一到一个文本到文本生成框架中,通过多任务培训使用解释作为额外的监督.
主要成果:
- 学习解释在少数镜头设置中,在各种生物医学NLP任务中显著提高性能.
- 在低资源场景中,拟议的方法在强有力的基线模型中表现高达6.41%.
- 与较大的LLM相比,220M LetEx模型显示出更高的推理解释能力.
结论:
- 可解释性,特别是通过学习解释,对于推进生物医学的少量学习至关重要.
- LetEx-Learning方法为数据稀缺的生物医学NLP挑战提供了一个有希望的解决方案.
- 利用LLM生成的解释可以提高模型性能和在专业领域的推理能力.
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