While large language models (LLMs) have achieved impressive performance across diverse tasks, recent studies showcase that causal LLMs suffer from the "reversal curse". It is a typical example that the model knows "A's father is B", but is unable to reason "B's child is A". This limitation poses a challenge to the advancement of artificial general intelligence (AGI), as it suggests a gap in the models' ability to comprehend and apply bidirectional reasoning. In this paper, we first conduct substantial evaluation and identify that the root cause of the reversal curse lies in the different word order between the training and inference stage, namely, the poor ability of causal language models to predict antecedent words within the training data. Accordingly, permutation on the training data is considered as a potential solution, since this can make the model predict antecedent words or tokens. However, previous permutation methods may disrupt complete phrases or entities, thereby posing challenges for the model to comprehend and learn from training data. To address this issue, we propose Semantic-aware Permutation Training (SPT), which addresses this issue by segmenting the training sentences into semantic units (i.e., entities or phrases) with an assistant language model and permuting these units before feeding into the model. Extensive experiments demonstrate that SPT effectively mitigates the reversal curse since the performance on reversed questions approximates that on the forward ones, and significantly advances the performance of existing works.

翻译：尽管大语言模型（LLMs）在各类任务中展现出惊人性能，但近期研究表明因果LLMs存在"反转诅咒"现象。典型案例为：模型已知"A的父亲是B"，却无法推导出"B的孩子是A"。这一局限性对通用人工智能（AGI）的发展构成挑战，揭示出模型在理解与应用双向推理能力上的缺陷。本文首先通过系统性评估，发现反转诅咒的根本原因在于训练阶段与推理阶段的词序差异，即因果语言模型预测训练数据中前置词的能力薄弱。据此，训练数据的排列重组被视为潜在解决方案，因其能使模型预测前置词或令牌。然而，传统排列方法可能破坏完整短语或实体结构，导致模型难以理解并学习训练数据。为解决该问题，我们提出语义感知排列训练（SPT）：通过辅助语言模型将训练语句分割为语义单元（如实体或短语），并在输入模型前对这些单元进行排列。大量实验表明，SPT能有效缓解反转诅咒（使反向问题性能逼近正向问题），并显著提升现有工作表现。