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：虽然大型语言模型（LLMs）在各类任务中展现出卓越性能，但最新研究表明，因果语言模型存在"反向诅咒"问题。典型表现为：模型知晓"A的父亲是B"，却无法推理出"B的孩子是A"。这一局限对通用人工智能的发展构成挑战，揭示了模型在理解和应用双向推理能力方面的缺陷。本文首先通过系统性评估，确认反向诅咒的根源在于训练与推理阶段的词序差异，即因果语言模型对训练数据中前置词的预测能力不足。据此，对训练数据进行排列被视为潜在解决方案，因该方法可使模型预测前置词或词元。然而，现有排列方法可能破坏完整短语或实体结构，阻碍模型对训练数据的理解与学习。针对此问题，我们提出语义感知排列训练（SPT），通过辅助语言模型将训练句子分割为语义单元（如实体或短语），在输入模型前对这些单元进行排列。大量实验表明，SPT能有效缓解反向诅咒——模型在反向问题上的表现已接近正向问题，且显著优于现有方法的性能。