Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
This work addresses the challenge of assessing reasoning abilities in AI models, which is crucial for advancing trustworthy AI systems, though it is incremental in nature.
The paper tackled the problem of evaluating the general deductive reasoning capacity of large language models (LLMs) by testing their ability to generalize to more complex proofs using out-of-distribution examples, finding that while LLMs can handle compositional proofs, they struggle with longer proofs and require explicit demonstrations for certain proof types like proof by cases and contradiction.
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity. Recent studies have shown that large language models (LLMs) possess some abstract deductive reasoning ability given chain-of-thought prompts. However, they have primarily been tested on proofs using modus ponens or of a specific size, and from the same distribution as the in-context examples. To measure the general deductive reasoning ability of LLMs, we test on a broad set of deduction rules and measure their ability to generalize to more complex proofs from simpler demonstrations from multiple angles: depth-, width-, and compositional generalization. To facilitate systematic exploration, we construct a new synthetic and programmable reasoning dataset that enables control over deduction rules and proof complexity. Our experiments on four LLMs of various sizes and training objectives show that they are able to generalize to compositional proofs. However, they have difficulty generalizing to longer proofs, and they require explicit demonstrations to produce hypothetical subproofs, specifically in proof by cases and proof by contradiction.