研究大模型解释结果对训练随机性的敏感度，发现任务影响最大。
Sensivity of LLMs' Explanations to the Training Randomness:Context, Class & Task Dependencies
- 分析上下文、类别和任务对模型解释敏感度的影响
- 任务差异导致解释变化最显著，类别次之，上下文影响最小
- 适合关注模型可解释性与稳定性的研究人员
Transformer 模型已成为自然语言处理的核心。然而，解释其决策仍具挑战性。最近研究表明，同一模型在相同数据上使用不同随机种子训练，可能产生截然不同的解释。本文探究了（句法）上下文、待学习类别及任务如何影响解释对随机性的敏感度。结果表明三者均具有统计显著影响：上下文影响最小，类别中等，任务影响最大。
原文摘要 · Abstract (English)
Transformer models are now a cornerstone in natural language processing. Yet, explaining their decisions remains a challenge. It was shown recently that the same model trained on the same data with a different randomness can lead to very different explanations. In this paper, we investigate how the (syntactic) context, the classes to be learned and the tasks influence this explanations' sensitivity to randomness. We show that they all have statistically significant impact: smallest for the (syntactic) context, medium for the classes and largest for the tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。