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reasoning steps

Reasoning steps are the discrete, sequential intermediate deductions, calculations, or assertions that connect an initial problem or premise to its final conclusion. In problem-solving, formal logic, and artificial intelligence, breaking complex tasks into a structured sequence of intermediate steps makes the underlying rationale explicit and transparent. In computational reasoning, each step represents a logical transition that builds upon prior statements or retrieved evidence to advance toward an answer. This step-by-step decomposition allows both human evaluators and automated systems to trace the progression of an argument, assess the relevance and factual attribution of individual claims, and detect or correct errors at specific points in the thought process rather than only evaluating the final output.

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A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains

A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains

Alon Jacovi, Yonatan Bitton, Bernd Bohnet, Jonathan Herzig, Or Honovich, Michael Tseng, Michael Collins, Roee Aharoni, Mor Geva

OrganizationsBar-Ilan UniversityGoogleTel Aviv University

Why you should read this

Presents REVEAL, a benchmark dataset equipped with step-level annotations for relevance, evidence attribution, and logical correctness to systematically evaluate how well automatic verifiers detect errors in language model reasoning chains.

Prompting language models to provide step-by-step answers (e.g., “Chain-of-Thought”) is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusses automatic methods to verify reasoning steps to evaluate and improve their correctness. However, no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods, hindering progress in this direction. We introduce REVEAL: Reasoning Verification Evaluation, a new dataset to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question answering settings. REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model’s answer, across a wide variety of datasets and state-of-the-art language models. Available at reveal-dataset.github.io.

Added

2026-10-03