---
title: "Self-Contrast — paper by Zhang et al. (2024)"
description: "To address this, they propose Self-Contrast. 95 references, cited by 48 papers here. Among them: 27."
authors: ["Wenqi Zhang", "Yongliang Shen", "Linjuan Wu", "Qiuying Peng", "Jun Wang", "Yueting Zhuang", "Weiming Lu"]
year: 2024
venue: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2024.acl-long.197/"
doi: "10.18653/v1/2024.acl-long.197"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2024-self-contrast-better-reflection-through/"
---
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# Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives

**Authors:** Wenqi Zhang, [Yongliang Shen](https://md.research.elamur.ai/free/en/authors/yongliang-shen/), Linjuan Wu, Qiuying Peng, [Jun Wang](https://md.research.elamur.ai/free/en/authors/jun-wang/), [Yueting Zhuang](https://md.research.elamur.ai/free/en/authors/yueting-zhuang/) and [Weiming Lu](https://md.research.elamur.ai/free/en/authors/weiming-lu/) · **Year:** 2024 · **Venue:** [Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.acl-long.197/) · [PDF](https://aclanthology.org/2024.acl-long.197.pdf) · [DOI 10.18653/v1/2024.acl-long.197](https://doi.org/10.18653/v1/2024.acl-long.197) · [OpenAlex](https://openalex.org/W4402672090)

**Citations:** 27 citations (OpenAlex, 2026-10-14) · [48](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-contrast-better-reflection-through/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper asks why large language models rarely fix their own mistakes when they reflect without outside feedback. The authors tested GPT-4, GPT-3.5, davinci-003 and Llama2 (7B/13B/70B) on GSM8K, SVAMP and the CommonMT translation set. Accuracy and BLEURT scores barely changed after standard self-reflection. Manual review of the self-generated critiques showed the cause. Models tend either to insist that a flawed answer is fine or to give a different verdict each time they judge the same answer.

To address this, they propose Self-Contrast. The model writes its own set of prompts that approach the task from different angles and produces a solution for each. It then compares the most divergent solutions and turns their differences into a checklist of points to re-verify before revising. Because comparing outputs is easier than judging one answer in isolation, this yields better and more stable corrections on both reasoning and translation. The work suggests that a model can reflect usefully without external signals if it critiques by contrast.

## Key points
- With plain self-reflection, only 15.1% of wrong initial answers got corrected. Feedback on failed cases was overconfident in 46.7% and inconsistent in 45.7% of instances.
- On GSM8K, correct-to-wrong revisions (52) roughly cancelled out wrong-to-correct ones (48). Wrong answers that stayed wrong numbered 269.
- Swapping direct evaluation for contrastive evaluation raised GPT-3.5 on GSM8K from 75.8 to 77.5 and made the gain far more significant (p from 0.66 to 0.09).
- Contrasting a correct with an incorrect solution improved results by 13.5%. Contrasting two solutions with different errors improved them by 5.4%. Pairs sharing the same error gave no benefit.
- The models usually generated about four solving perspectives. For Llama2-7B, Self-Contrast slightly trailed self-consistency, and the authors suggest tools like difflib for comparison as future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Zhejiang University](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) — University, Hangzhou, China · 46 works · 11,542 citations

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## Cite

Cite as: Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang and Weiming Lu. 2024. Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2024self,
  title = {{Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives}},
  author = {Wenqi Zhang, Yongliang Shen, Linjuan Wu, Qiuying Peng, Jun Wang, Yueting Zhuang and Weiming Lu},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.197},
  url = {https://doi.org/10.18653/v1/2024.acl-long.197}
}
```

- [← previous PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
- [next → Self-Reflection in LLM Agents: Effects on Problem-Solving Performance](https://md.research.elamur.ai/free/en/papers/renze-2024-self-reflection-llm-agents-effects/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/4/)

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*Updated 24 October 2026*