Verifying the Steps of Deductive Reasoning Chains

As Large Language Models penetrate everyday life more and more, it becomes essential to measure the correctness of their output. In this paper, we propose a novel task: the automatic verification of individual reasoning steps in a logical deductive Chain-of-Thought. This task addresses two well-known problems of LLMs, hallucination and incorrect reasoning. We propose a new dataset of logical reasoning chains, in which the individual deduction steps have been manually annotated for soundness, and benchmark several methods on it. We find that LLMs can detect unsound reasoning steps fairly well, but argue that verification has to be performed by transparent methods instead. We test symbolic methods, but find that they under-perform. We develop a neuro-symbolic baseline called VANESSA that comes closer to the performance of LLMs. The question is how you arrive at your opinions and not what your opinions are.