---
title: "Two Failures of Self-Consistency in the Multi-Step Reasoning of"
description: "The authors formally define two new kinds of self-consistency. 20 references, cited by 18 papers here."
authors: ["Angelica Chen", "Jason Phang", "Alicia Parrish", "Vishakh Padmakumar", "Chen Zhao", "Samuel R. Bowman", "Kyunghyun Cho"]
year: 2023
venue: "arXiv:2305.14279 · Transactions on Machine Learning Research (2024)"
source: "https://arxiv.org/abs/2305.14279"
doi: "10.48550/arxiv.2305.14279"
arxiv: "2305.14279"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2023-two-failures-self-consistency-multi/"
---
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# Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

**Authors:** [Angelica Chen](https://md.research.elamur.ai/free/en/authors/angelica-chen/), [Jason Phang](https://md.research.elamur.ai/free/en/authors/jason-phang/), [Alicia Parrish](https://md.research.elamur.ai/free/en/authors/alicia-parrish/), [Vishakh Padmakumar](https://md.research.elamur.ai/free/en/authors/vishakh-padmakumar/), [Chen Zhao](https://md.research.elamur.ai/free/en/authors/chen-zhao/), [Samuel R. Bowman](https://md.research.elamur.ai/free/en/authors/samuel-r-bowman/) and [Kyunghyun Cho](https://md.research.elamur.ai/free/en/authors/kyunghyun-cho/) · **Year:** 2023 · **Venue:** arXiv:2305.14279 · Transactions on Machine Learning Research (2024) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2305.14279) · [PDF](https://arxiv.org/pdf/2305.14279) · [DOI 10.48550/arxiv.2305.14279](https://doi.org/10.48550/arxiv.2305.14279) · [arXiv 2305.14279](https://arxiv.org/abs/2305.14279) · [OpenAlex](https://openalex.org/W4378509375)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [18](https://md.research.elamur.ai/free/en/papers/chen-2023-two-failures-self-consistency-multi/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Evaluations of LLMs usually check whether answers are correct, not whether a model's answers agree with each other. This paper argues that agreement matters for multi-step reasoning, where a final answer depends on answers to sub-steps. The authors formally define two new kinds of self-consistency. **Hypothetical consistency** asks whether a model can predict what it would output for a prompt when asked indirectly. **Compositional consistency** asks whether the final output stays the same when an intermediate sub-question is replaced by the model's own answer to it.

For hypothetical consistency, the authors use a multiple-choice setup, which avoids having to judge semantic equivalence automatically. For compositional consistency, they test on arithmetic expressions and GeoQuery semantic parsing.

Several GPT-3 and GPT-4 variants show low consistency of both kinds. Larger models do somewhat better. The results suggest that LLM outputs on complex compositional tasks should not be trusted without careful empirical checking.

## Key points
- **Hypothetical consistency setup:**
  - Prompts are sampled from Wikipedia and DailyDialog.
  - Each prompt is rewritten with four hypothetical templates, and results are averaged across templates to reduce sensitivity to wording.
  - The model chooses among five options: the original dataset continuation, its own greedy completion, and three completions from other models.
- **Arithmetic errors:** about 90% of compositional inconsistencies happen when the sub-expression is answered correctly but the final answer does not match the answer to the substituted version.
- **GeoQuery errors:**
  - Roughly 59% of inconsistencies occur because the subtree's parse is not reused inside the parent parse.
  - The remaining ~41% come from an incorrect parse of the child query.
- **Correctness vs. consistency on arithmetic:** across four model sizes, the two are roughly linearly related. All models except davinci-001 are slightly more correct than consistent.
- **Relation to prior work:** the study builds on earlier work by Elazar et al. (2021) and Jang et al. (2022) that measured consistency across paraphrases. It also ties into claims by Dziri et al. (2023) that Transformers are inherently limited on compositional tasks.

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

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## Cite

Cite as: Angelica Chen, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman and Kyunghyun Cho. 2023. Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs. arXiv:2305.14279 · Transactions on Machine Learning Research (2024).

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

```bibtex
@misc{chen2023two,
  title = {{Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs}},
  author = {Angelica Chen, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman and Kyunghyun Cho},
  year = {2023},
  doi = {10.48550/arxiv.2305.14279},
  eprint = {2305.14279},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.14279}
}
```

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## References

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*Updated 30 October 2026*