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step relevance

Step relevance is a quality metric in multi-step natural language reasoning that measures whether an individual intermediate step contributes meaningfully and directly toward solving the target question or task. Unlike end-to-end evaluation metrics that judge an entire response as a single unit, step relevance operates at a fine-grained level to verify that each distinct link in a chain of thought is pertinent rather than tangential, redundant, or distracting. Evaluating the relevance of discrete steps enables automated verifiers and language models to detect drifting arguments, filter out unnecessary reasoning paths, and ensure that every intermediate deduction actively supports the progression toward a valid final conclusion.

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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