语言模型在路径星任务中表现差，研究发现是表示缺陷导致的。
The Mystery of the Pathological Path-star Task for Language Models
- 用结构化样本正则化提升模型对路径星图的表征能力
- 编码器模型在特定设置下可稳定解决该任务
- 证明了该任务理论上可解，揭示了训练范式局限性
路径星任务是一种最小化任务，旨在体现语言模型的能力局限（Bachmann and Nagarajan, 2024）。任务基于路径星图结构，多个分支从单一起点节点辐射而出，每个节点唯一。给定起始节点和目标节点（位于某一分支末端），任务是生成包含该目标节点的完整路径。人类可轻松完成，但语言模型性能未超过随机基线。作者推测这源于教师强制与下一个词预测范式的缺陷。本文证明在替代教师强制设置下该任务可学习，且问题部分源于表示缺陷。提出一种使用相同图但不同目标节点的结构化样本正则化方法，在多种模型上均提升性能。提供RASP形式化证明，表明该任务理论上可解。最终发现编码器仅模型在某些设定下可一致解决该任务。
原文摘要 · Abstract (English)
The recently introduced path-star task is a minimal task designed to exemplify limitations to the abilities of language models (Bachmann and Nagarajan, 2024). It involves a path-star graph where multiple arms radiate from a single starting node and each node is unique. Given the start node and a specified target node that ends an arm, the task is to generate the arm containing that target node. This is straightforward for a human but surprisingly difficult for language models, which did not outperform the random baseline. The authors hypothesized this is due to a deficiency in teacher-forcing and the next-token prediction paradigm. We demonstrate the task is learnable using teacher-forcing in alternative settings and that the issue is partially due to representation. We introduce a regularization method using structured samples of the same graph but with differing target nodes, improving results across a variety of model types. We provide RASP proofs showing the task is theoretically solvable. Finally, we find settings where an encoder-only model can consistently solve the task.
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