mlsanityData to 7 Oct 2026

Can We Verify Step by Step for Incorrect Answer Detection?

arXiv 2402.10528 ↗First posted 16 Feb 2024cs.CL cs.AIcited by 15

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

Chain-of-Thought (CoT) prompting has marked a significant advancement in enhancing the reasoning capabilities of large language models (LLMs). Previous studies have developed various extensions of CoT, which focus primarily on enhancing end-task performance. In addition, there has been research on assessing the quality of reasoning chains in CoT. This raises an intriguing question: Is it possible to predict the accuracy of LLM outputs by scrutinizing the reasoning chains they generate? To answer this research question, we introduce a benchmark, R2PE, designed specifically to explore the relationship between reasoning chains and performance in various reasoning tasks spanning five different domains. This benchmark aims to measure the falsehood of the final output of LLMs based on the reasoning steps. To make full use of information in multiple reasoning chains, we propose the process discernibility score (PDS) framework that beats the answer-checking baseline by a large margin. Concretely, this resulted in an average of 5.1% increase in the F1 score and 2.97% improvement in AUC-PR across all 45 subsets within R2PE. We further demonstrate our PDS's efficacy in advancing open-domain QA accuracy.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 50 of its 69 references that are in the map. First posted on arXiv 16 Feb 2024.

Builds on

50 of 69 references.

Closest ancestors · 14

Same direction, newest first

  1. 2023Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation7 Nov
  2. 2023Generalizable Chain-of-Thought Prompting in Mixed-task Scenarios with Large Language Models10 Oct
  3. 2023Better Zero-Shot Reasoning with Self-Adaptive Prompting23 May
  4. 2023Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling17 May
  5. 2023Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework5 May
  6. 2023Progressive-Hint Prompting Improves Reasoning in Large Language Models19 Apr
  7. 2023Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data24 Feb
  8. 2023Active Prompting with Chain-of-Thought for Large Language Models23 Feb
  9. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks22 Nov
  10. 2022Automatic Chain of Thought Prompting in Large Language Models7 Oct
  11. 2022Complexity-Based Prompting for Multi-Step Reasoning3 Oct
  12. 2022Large Language Models are Zero-Shot Reasoners24 May
2 more
  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 36

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  7. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  8. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  9. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  10. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  11. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  12. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
24 more
  1. 2022Language Models (Mostly) Know What They Knowcited by 1,783
  2. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  3. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  4. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
  5. 2023Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generationcited by 795
  6. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  7. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  8. 2023MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Modelscited by 724
  9. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  10. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
  11. 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thoughtcited by 334
  12. 2020Unsupervised Quality Estimation for Neural Machine Translationcited by 210
  13. 2022ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoningcited by 175
  14. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  15. 2023Answering Questions by Meta-Reasoning over Multiple Chains of Thoughtcited by 87
  16. 2023ReCEval: Evaluating Reasoning Chains via Correctness and Informativenesscited by 76
  17. 2022NaturalProver: Grounded Mathematical Proof Generation with Language Modelscited by 72
  18. 2018ROUGE 2.0: Updated and Improved Measures for Evaluation of Summarization Taskscited by 66
  19. 2023Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Modelscited by 36
  20. 2021A Study of Automatic Metrics for the Evaluation of Natural Language Explanationscited by 34
  21. 2023Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logiccited by 34
  22. 2023Agent Instructs Large Language Models to be General Zero-Shot Reasonerscited by 32
  23. 2023SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluationcited by 27
  24. 2023Natural Language Embedded Programs for Hybrid Language Symbolic Reasoningcited by 15

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Citing papers per year: 2024: 5; 2025: 10.

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  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  2. 2024Small Language Models Need Strong Verifiers to Self-Correct Reasoningcited by 85
  3. 2025UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Modelscited by 45
  4. 2025Detection and Mitigation of Hallucination in Large Reasoning Models: A Mechanistic Perspectivecited by 32
  5. 2024Can LLMs Solve longer Math Word Problems Better?cited by 22
  6. 2024FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Modelcited by 18
  7. 2024S^3cMath: Spontaneous Step-level Self-correction Makes Large Language Models Better Mathematical Reasonerscited by 17
  8. 2025Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verificationcited by 16
  9. 2025UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Modelscited by 13
  10. 2024VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoningcited by 6
  11. 2025MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Taskcited by 5
  12. 2025Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solvingcited by 4
3 more
  1. 2025Cascaded Self-Evaluation Augmented Training for Lightweight Multimodal LLMscited by 2
  2. 2025Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuningcited by 2
  3. 2025Towards Automated Smart Contract Generation: Evaluation, Benchmarking, and Retrieval-Augmented Repaircited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Chain-of-Probe: Examining the Necessity and Accuracy of CoT Step-by-Step
  2. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
  3. 2023Faithful Chain-of-Thought Reasoning
  4. 2025Early Stopping Chain-of-thoughts in Large Language Models
  5. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
  6. 2024Chain-of-Thought Reasoning Without Prompting
  7. 2022Large Language Models are Better Reasoners with Self-Verification
  8. 2024A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains