# Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information

arXiv: https://arxiv.org/abs/2410.04463 · HTML: https://mlsanity.com/paper/2410.04463/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning.
- Builds on 21 of its 43 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Placed in

- LLM reasoning / [Prompting and chain-of-thought reasoning](https://mlsanity.com/lr/prompting-and-chain-of-thought-reasoning/)

## Builds on (most cited first, 21 of 21)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Solving General Arithmetic Word Problems](https://mlsanity.com/paper/1608.01413/) (2016)
- [Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition](https://mlsanity.com/paper/2501.03230/) (2024)
- [Solving Math Word Problems by Combining Language Models With Symbolic Solvers](https://mlsanity.com/paper/2304.09102/) (2023)
- [Self-Discover: Large Language Models Self-Compose Reasoning Structures](https://mlsanity.com/paper/2402.03620/) (2024)
- [Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation](https://mlsanity.com/paper/2307.15337/) (2023)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [Faithful Logical Reasoning via Symbolic Chain-of-Thought](https://mlsanity.com/paper/2405.18357/) (2024)
- [Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages](https://mlsanity.com/paper/2310.14799/) (2023)
- [Reasoning Implicit Sentiment with Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.11255/) (2023)
- [Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning](https://mlsanity.com/paper/2403.20046/) (2024)
- [In-Context Principle Learning from Mistakes](https://mlsanity.com/paper/2402.05403/) (2024)
- [Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts](https://mlsanity.com/paper/2310.14628/) (2023)
- [The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models](https://mlsanity.com/paper/2305.14999/) (2023)
- [AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thought](https://mlsanity.com/paper/2406.13940/) (2024)

## Built on by (most cited first, 10 of 10)

- [A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?](https://mlsanity.com/paper/2503.24235/) (2025)
- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models](https://mlsanity.com/paper/2505.13143/) (2025)
- [ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models](https://mlsanity.com/paper/2507.09876/) (2025)
- [CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models](https://mlsanity.com/paper/2505.19108/) (2025)
- [Latent Visual Cache for Video Reasoning](https://mlsanity.com/paper/2607.02607/) (2026)
- [SinkTrack: Attention Sink based Context Anchoring for Large Language Models](https://mlsanity.com/paper/2604.10027/) (2026)
- [What Factors Affect LLMs and RLLMs in Financial Question Answering?](https://mlsanity.com/paper/2507.08339/) (2025)
- [DiagnoLLM: A Hybrid Bayesian Neural Language Framework for Interpretable Disease Diagnosis](https://mlsanity.com/paper/2511.05810/) (2025)
- [IFDNS: An Iterative Feedback-Driven Neuro-Symbolic Method for Faithful Logical Reasoning](https://mlsanity.com/paper/2601.07464/) (2026)

## Abstract (arXiv)

Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of approaches focuses on the iterative enhancement of LLMs by continuously verifying and refining their reasoning outputs for desired quality. Despite its impressive results, this paradigm faces two critical issues: (1) Simple verification methods: The current paradigm relies solely on a single verification method. (2) Wrong Information Ignorance: Traditional paradigms directly ignore wrong information during reasoning and refine the logic paths from scratch each time. To address these challenges, we propose Wrong-of-Thought (WoT), which includes two core modules: (1) Multi-Perspective Verification: A multi-perspective verification method for accurately refining the reasoning process and result, and (2) Wrong Information Utilization: Utilizing wrong information to alert LLMs and reduce the probability of LLMs making same mistakes. Experiments on 8 popular datasets and 5 LLMs demonstrate that WoT surpasses all previous baselines. In addition, WoT exhibits powerful capabilities in difficult computation tasks.