mlsanityData to 14 Oct 2026

Error Reflection Prompting: Can Large Language Models Successfully Understand Errors?

arXiv 2508.16729 ↗First posted 22 Aug 2025cs.CLcited by 0

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

Abstract (arXiv)

Prompting methods for language models, such as Chain-of-thought (CoT), present intuitive step-by-step processes for problem solving. These methodologies aim to equip models with a better understanding of the correct procedures for addressing a given task. Despite these advancements, CoT lacks the ability of reflection and error correction, potentially causing a model to perpetuate mistakes and errors. Therefore, inspired by the human ability for said tasks, we propose Error Reflection Prompting (ERP) to further enhance reasoning in language models. Building upon CoT, ERP is a method comprised of an incorrect answer, error recognition, and a correct answer. This process enables the model to recognize types of errors and the steps that lead to incorrect answers, allowing the model to better discern which steps to avoid and which to take. The model is able to generate the error outlines itself with automated ERP generation, allowing for error recognition and correction to be integrated into the reasoning chain and produce scalability and reliability in the process. The results demonstrate that ERP serves as a versatile supplement to conventional CoT, ultimately contributing to more robust and capable reasoning abilities along with increased interpretability in how models ultimately reach their errors.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 9 of its 16 references that are in the map. First posted on arXiv 22 Aug 2025.

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  1. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting7 May

Other references · 8

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  3. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  4. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 743
  5. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 650
  6. 2023OpenAGI: When LLM Meets Domain Expertscited by 197
  7. 2023Should We Learn Most Likely Functions or Parameters?cited by 12
  8. 2022Towards Automated Error Analysis: Learning to Characterize Errorscited by 3

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  2. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
  3. 2022Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters
  4. 2025Reasoning Can Hurt the Inductive Abilities of Large Language Models
  5. 2025Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting
  6. 2023Stress Testing Chain-of-Thought Prompting for Large Language Models
  7. 2023Large Language Models as Analogical Reasoners
  8. 2025When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs