Large Language Models Cannot Self-Correct Reasoning Yet Article Swipe
Jie Huang
,
Xinyun Chen
,
Swaroop Mishra
,
Huaixiu Zheng
,
Adams Wei Yu
,
Xinying Song
,
Denny Zhou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.01798
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.01798
Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues. Building upon this premise, this paper critically examines the role and efficacy of self-correction within LLMs, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities, without the crutch of external feedback. In the context of reasoning, our research indicates that LLMs struggle to self-correct their responses without external feedback, and at times, their performance even degrades after self-correction. Drawing from these insights, we offer suggestions for future research and practical applications in this field.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.01798
- https://arxiv.org/pdf/2310.01798
- OA Status
- green
- Cited By
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387355948
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387355948Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2310.01798Digital Object Identifier
- Title
-
Large Language Models Cannot Self-Correct Reasoning YetWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-03Full publication date if available
- Authors
-
Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Zheng, Adams Wei Yu, Xinying Song, Denny ZhouList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.01798Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.01798Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.01798Direct OA link when available
- Concepts
-
Computer science, Natural language processing, Artificial intelligence, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
31Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 14, 2023: 8Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387355948 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2310.01798 |
| ids.doi | https://doi.org/10.48550/arxiv.2310.01798 |
| ids.openalex | https://openalex.org/W4387355948 |
| fwci | |
| type | preprint |
| title | Large Language Models Cannot Self-Correct Reasoning Yet |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10028 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.5723000168800354 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Topic Modeling |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.5430935621261597 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C204321447 |
| concepts[1].level | 1 |
| concepts[1].score | 0.4016731381416321 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[1].display_name | Natural language processing |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.37288403511047363 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C41895202 |
| concepts[3].level | 1 |
| concepts[3].score | 0.33556410670280457 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[3].display_name | Linguistics |
| concepts[4].id | https://openalex.org/C138885662 |
| concepts[4].level | 0 |
| concepts[4].score | 0.09281671047210693 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[4].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.5430935621261597 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/natural-language-processing |
| keywords[1].score | 0.4016731381416321 |
| keywords[1].display_name | Natural language processing |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.37288403511047363 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/linguistics |
| keywords[3].score | 0.33556410670280457 |
| keywords[3].display_name | Linguistics |
| keywords[4].id | https://openalex.org/keywords/philosophy |
| keywords[4].score | 0.09281671047210693 |
| keywords[4].display_name | Philosophy |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2310.01798 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2310.01798 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2310.01798 |
| locations[1].id | doi:10.48550/arxiv.2310.01798 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2310.01798 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5101628289 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6126-7214 |
| authorships[0].author.display_name | Jie Huang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Huang, Jie |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5101977008 |
| authorships[1].author.orcid | https://orcid.org/0009-0001-9700-8951 |
| authorships[1].author.display_name | Xinyun Chen |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Chen, Xinyun |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5063722751 |
| authorships[2].author.orcid | https://orcid.org/0009-0001-6413-7001 |
| authorships[2].author.display_name | Swaroop Mishra |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mishra, Swaroop |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5003807260 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Huaixiu Zheng |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zheng, Huaixiu Steven |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5045729519 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Adams Wei Yu |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yu, Adams Wei |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5108138075 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Xinying Song |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Song, Xinying |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5061512999 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Denny Zhou |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Zhou, Denny |
| authorships[6].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2310.01798 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Large Language Models Cannot Self-Correct Reasoning Yet |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10028 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.5723000168800354 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Topic Modeling |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W2358668433, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W2382290278, https://openalex.org/W2478288626, https://openalex.org/W4391913857, https://openalex.org/W3204019825 |
| cited_by_count | 31 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 9 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 14 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 8 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2310.01798 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2310.01798 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2310.01798 |
| primary_location.id | pmh:oai:arXiv.org:2310.01798 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2310.01798 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2310.01798 |
| publication_date | 2023-10-03 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 31 |
| abstract_inverted_index.a | 7, 39 |
| abstract_inverted_index.In | 99 |
| abstract_inverted_index.an | 79 |
| abstract_inverted_index.as | 6, 38 |
| abstract_inverted_index.at | 118 |
| abstract_inverted_index.in | 139 |
| abstract_inverted_index.is | 72 |
| abstract_inverted_index.of | 27, 56, 75, 96, 102 |
| abstract_inverted_index.on | 62, 89 |
| abstract_inverted_index.to | 41, 69, 82, 110 |
| abstract_inverted_index.we | 130 |
| abstract_inverted_index.LLM | 80 |
| abstract_inverted_index.and | 25, 54, 66, 117, 136 |
| abstract_inverted_index.for | 133 |
| abstract_inverted_index.has | 35 |
| abstract_inverted_index.its | 63, 84, 90 |
| abstract_inverted_index.our | 70, 104 |
| abstract_inverted_index.the | 23, 52, 73, 94, 100 |
| abstract_inverted_index.LLMs | 108 |
| abstract_inverted_index.been | 36 |
| abstract_inverted_index.even | 122 |
| abstract_inverted_index.from | 127 |
| abstract_inverted_index.have | 4 |
| abstract_inverted_index.role | 53 |
| abstract_inverted_index.text | 13 |
| abstract_inverted_index.that | 107 |
| abstract_inverted_index.this | 46, 48, 140 |
| abstract_inverted_index.true | 64 |
| abstract_inverted_index.upon | 45 |
| abstract_inverted_index.with | 10 |
| abstract_inverted_index.LLMs, | 59 |
| abstract_inverted_index.Large | 0 |
| abstract_inverted_index.after | 124 |
| abstract_inverted_index.based | 87 |
| abstract_inverted_index.light | 61 |
| abstract_inverted_index.offer | 131 |
| abstract_inverted_index.paper | 49 |
| abstract_inverted_index.their | 11, 28, 112, 120 |
| abstract_inverted_index.these | 42, 128 |
| abstract_inverted_index.(LLMs) | 3 |
| abstract_inverted_index.Models | 2 |
| abstract_inverted_index.across | 16 |
| abstract_inverted_index.crutch | 95 |
| abstract_inverted_index.field. | 141 |
| abstract_inverted_index.future | 134 |
| abstract_inverted_index.notion | 74 |
| abstract_inverted_index.remedy | 40 |
| abstract_inverted_index.solely | 88 |
| abstract_inverted_index.times, | 119 |
| abstract_inverted_index.within | 58 |
| abstract_inverted_index.Central | 68 |
| abstract_inverted_index.Drawing | 126 |
| abstract_inverted_index.context | 101 |
| abstract_inverted_index.correct | 83 |
| abstract_inverted_index.emerged | 5 |
| abstract_inverted_index.initial | 85 |
| abstract_inverted_index.issues. | 43 |
| abstract_inverted_index.persist | 21 |
| abstract_inverted_index.various | 17 |
| abstract_inverted_index.whereby | 78 |
| abstract_inverted_index.without | 93, 114 |
| abstract_inverted_index.Building | 44 |
| abstract_inverted_index.Language | 1 |
| abstract_inverted_index.accuracy | 24 |
| abstract_inverted_index.attempts | 81 |
| abstract_inverted_index.concerns | 20 |
| abstract_inverted_index.content. | 30 |
| abstract_inverted_index.degrades | 123 |
| abstract_inverted_index.efficacy | 55 |
| abstract_inverted_index.examines | 51 |
| abstract_inverted_index.external | 97, 115 |
| abstract_inverted_index.inherent | 91 |
| abstract_inverted_index.premise, | 47 |
| abstract_inverted_index.proposed | 37 |
| abstract_inverted_index.research | 105, 135 |
| abstract_inverted_index.shedding | 60 |
| abstract_inverted_index.struggle | 109 |
| abstract_inverted_index.feedback, | 116 |
| abstract_inverted_index.feedback. | 98 |
| abstract_inverted_index.generated | 29 |
| abstract_inverted_index.indicates | 106 |
| abstract_inverted_index.insights, | 129 |
| abstract_inverted_index.intrinsic | 76 |
| abstract_inverted_index.potential | 65 |
| abstract_inverted_index.practical | 137 |
| abstract_inverted_index.regarding | 22 |
| abstract_inverted_index.responses | 86, 113 |
| abstract_inverted_index.critically | 50 |
| abstract_inverted_index.generation | 14 |
| abstract_inverted_index.reasoning, | 103 |
| abstract_inverted_index.technology | 9 |
| abstract_inverted_index.performance | 121 |
| abstract_inverted_index.suggestions | 132 |
| abstract_inverted_index.applications | 138 |
| abstract_inverted_index.capabilities | 15 |
| abstract_inverted_index.contemporary | 32 |
| abstract_inverted_index.limitations. | 67 |
| abstract_inverted_index.methodology, | 33 |
| abstract_inverted_index.self-correct | 111 |
| abstract_inverted_index.unparalleled | 12 |
| abstract_inverted_index.Nevertheless, | 19 |
| abstract_inverted_index.applications. | 18 |
| abstract_inverted_index.capabilities, | 92 |
| abstract_inverted_index.investigation | 71 |
| abstract_inverted_index.groundbreaking | 8 |
| abstract_inverted_index.appropriateness | 26 |
| abstract_inverted_index.self-correction | 57 |
| abstract_inverted_index.self-correction, | 34, 77 |
| abstract_inverted_index.self-correction. | 125 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |