通过细粒度分析揭示语言模型训练中的隐藏学习阶段
Disaggregation Reveals Hidden Training Dynamics: The Case of Agreement Attraction
- 构建精细数据集，分条件拆解模型错误模式
- 发现模型在不同训练阶段依赖词频与局部上下文
- 适合研究模型学习机制与语法泛化能力的学者
语言模型通常生成语法正确的文本，但在特定句法语境下更易出错。借鉴心理语言学范式，我们对不同句法语境下的错误进行了细粒度分析。通过在精心构建的数据集上分条件拆解并跟踪模型在训练过程中的表现，我们揭示了语言模型在语法学习过程中存在多个阶段性行为模式。具体而言，模型在不同训练阶段表现出对词频、局部上下文等启发式规则的依赖，而非统一的语法规则。我们认为，这种分条件分析方法可作为理解语言模型中间学习阶段、整体训练动态及具体泛化能力的强大工具。
原文摘要 · Abstract (English)
Language models generally produce grammatical text, but they are more likely to make errors in certain contexts. Drawing on paradigms from psycholinguistics, we carry out a fine-grained analysis of those errors in different syntactic contexts. We demonstrate that by disaggregating over the conditions of carefully constructed datasets and comparing model performance on each over the course of training, it is possible to better understand the intermediate stages of grammatical learning in language models. Specifically, we identify distinct phases of training where language model behavior aligns with specific heuristics such as word frequency and local context rather than generalized grammatical rules. We argue that taking this approach to analyzing language model behavior more generally can serve as a powerful tool for understanding the intermediate learning phases, overall training dynamics, and the specific generalizations learned by language models.
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。