We are interested in understanding how well Transformer language models
(TLMs) can perform reasoning tasks when trained on knowledge encoded in the
form of natural language. We investigate their systematic generalization
abilities on a logical reasoning task in natural language, which involves
reasoning over relationships between entities grounded in first-order logical
proofs. Specifically, we perform soft theorem-proving by leveraging TLMs to
generate natural language proofs. We test the generated proofs for logical
consistency, along with the accuracy of the final inference. We observe
length-generalization issues when evaluated on longer-than-trained sequences.
However, we observe TLMs improve their generalization performance after being
exposed to longer, exhaustive proofs. In addition, we discover that TLMs are
able to generalize better using backward-chaining proofs compared to their
forward-chaining counterparts, while they find it easier to generate forward
chaining proofs. We observe that models that are not trained to generate proofs
are better at generalizing to problems based on longer proofs. This suggests
that Transformers have efficient internal reasoning strategies that are harder
to interpret. These results highlight the systematic generalization behavior of
TLMs in the context of logical reasoning, and we believe this work motivates
deeper inspection of their underlying reasoning strategies.

研究 Transformer 语言模型在自然语言中进行基于逻辑推理的任务，探究它们的系统泛化能力，发现其在逆向推理证明方面表现更优，并且发现没有经过证明生成训练的模型更适合处理长证明的问题。研究结果强调了 TLM 在逻辑推理中的系统泛化行为，并且对其核心推理策略的深入研究提出了启示。