Enhancing logical reasoning in language models: An investigation of the Capybara dataset.
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| Title: | Enhancing logical reasoning in language models: An investigation of the Capybara dataset. |
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| Authors: | Muñoz Guerrero, Luis Eduardo1, Ceballos, Yony Fernando2, Trejos Rojas, Luis David1 luis.trejos@utp.edu.co |
| Source: | Contemporary Educational Technology. Jul2025, Vol. 17 Issue 3, p1-14. 14p. |
| Subject Terms: | Language models, Linguistic models, Model-based reasoning, Inference (Logic), Chatbots, Logic |
| Abstract: | Recent progress made in conversational AI lays emphasis on the need for development of language models that possess solid logical reasoning skills and further extrapolated capabilities. An examination into this phenomenon investigates how well the Capybara dataset can improve one's ability to reason using language-based systems. Multiple cutting-edge linguistic models were fine-tuned using the Capybara corpus before assessing their performances on standard tasks demanding sophisticated reasoning. The comparison using different ways reveals that the logical reasoning of models improves and their ability to make inferences is enhanced. This research explores this further by considering what it means for developers who want more human-like machine conversation intelligence. We also see that this could become an invaluable tool when training reasoning-oriented language generating models. [ABSTRACT FROM AUTHOR] |
| Copyright of Contemporary Educational Technology is the property of Bastas Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 188037163 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing logical reasoning in language models: An investigation of the Capybara dataset. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Muñoz+Guerrero%2C+Luis+Eduardo%22">Muñoz Guerrero, Luis Eduardo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ceballos%2C+Yony+Fernando%22">Ceballos, Yony Fernando</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Trejos+Rojas%2C+Luis+David%22">Trejos Rojas, Luis David</searchLink><relatesTo>1</relatesTo><i> luis.trejos@utp.edu.co</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Contemporary+Educational+Technology%22">Contemporary Educational Technology</searchLink>. Jul2025, Vol. 17 Issue 3, p1-14. 14p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistic+models%22">Linguistic models</searchLink><br /><searchLink fieldCode="DE" term="%22Model-based+reasoning%22">Model-based reasoning</searchLink><br /><searchLink fieldCode="DE" term="%22Inference+%28Logic%29%22">Inference (Logic)</searchLink><br /><searchLink fieldCode="DE" term="%22Chatbots%22">Chatbots</searchLink><br /><searchLink fieldCode="DE" term="%22Logic%22">Logic</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recent progress made in conversational AI lays emphasis on the need for development of language models that possess solid logical reasoning skills and further extrapolated capabilities. An examination into this phenomenon investigates how well the Capybara dataset can improve one's ability to reason using language-based systems. Multiple cutting-edge linguistic models were fine-tuned using the Capybara corpus before assessing their performances on standard tasks demanding sophisticated reasoning. The comparison using different ways reveals that the logical reasoning of models improves and their ability to make inferences is enhanced. This research explores this further by considering what it means for developers who want more human-like machine conversation intelligence. We also see that this could become an invaluable tool when training reasoning-oriented language generating models. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Contemporary Educational Technology is the property of Bastas Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.30935/cedtech/16425 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Language models Type: general – SubjectFull: Linguistic models Type: general – SubjectFull: Model-based reasoning Type: general – SubjectFull: Inference (Logic) Type: general – SubjectFull: Chatbots Type: general – SubjectFull: Logic Type: general Titles: – TitleFull: Enhancing logical reasoning in language models: An investigation of the Capybara dataset. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Muñoz Guerrero, Luis Eduardo – PersonEntity: Name: NameFull: Ceballos, Yony Fernando – PersonEntity: Name: NameFull: Trejos Rojas, Luis David IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1309517X Numbering: – Type: volume Value: 17 – Type: issue Value: 3 Titles: – TitleFull: Contemporary Educational Technology Type: main |
| ResultId | 1 |
