通过问LLM问题,为语言神经科学制作可解释的嵌入式
Vinamra Benara1, Chandan Singh2, John X Morris3
1UC Berkeley.
Advances in neural information processing systems
|April 25, 2025
概括
我们介绍了可解释机器学习的问答嵌入式 (QA-Emb). QA-Emb使用是/否问题来创建可理解的文本嵌入,在神经科学应用中表现优于基线.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 提供了先进的文本嵌入,但缺乏可解释性,这对于神经科学等科学应用至关重要.
- 在科学中越来越多地使用LLM,因此需要方法来理解它们的内部表示.
研究的目的:
- 调查是否可以使用提示从LLM生成可解释的嵌入.
- 引入和评估一种用于创建可解释嵌入式的新方法,称为问答嵌入式 (QA-Emb).
主要方法:
- 开发了QA-Emb,其中每个特征对应于LLM对特定问题的对/否答案.
- 通过选择相关问题而不是优化模型重量来训练QA-Emb.
- 应用QA-Emb来预测功能磁共振成像 (fMRI) 语音对语言刺激的反应.
主要成果:
- 在预测fMRI反应方面,QA-Emb显著优于已知的可解释基线.
- 该方法在最少的问题集中实现了高性能.
- QA-Emb模型可以通过更简单的模型高效地近似.
结论:
- QA-Emb提供了一种灵活和可解释的方法,用于生成语义大脑表示的特征空间.
- 这种方法有助于具体化和评估我们对大脑如何处理语言的理解.
- 在自然语言处理 (NLP) 任务中,QA-Emb对更广泛的应用有希望.
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