Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/120092
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dc.contributorDepartment of Language Science and Technologyen_US
dc.creatorFeng, Zen_US
dc.creatorChen, Zen_US
dc.creatorMa, Jen_US
dc.creatorPo, YTen_US
dc.creatorChersoni, Een_US
dc.creatorLi, Ben_US
dc.date.accessioned2026-07-22T08:13:08Z-
dc.date.available2026-07-22T08:13:08Z-
dc.identifier.isbn979-8-89176-390-6en_US
dc.identifier.urihttp://hdl.handle.net/10397/120092-
dc.descriptionThe 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), San Diego, California, United States, July 2-7, 2026en_US
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguisticsen_US
dc.rights©2026 Association for Computational Linguisticsen_US
dc.rightsLicensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/)en_US
dc.rightsThe following publication Feng, Z., Chen, Z., Ma, J., Po, Y. T., Chersoni, E., & Li, B. (2026). Good arguments against the people pleasers: How reasoning Mitigates (yet masks) LLM Sycophancy. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 24536–24570 is available at https://doi.org/10.18653/v1/2026.acl-long.1126.en_US
dc.titleGood arguments against the people pleasers : how reasoning mitigates (yet masks) LLM sycophancyen_US
dc.typeConference Paperen_US
dc.identifier.spage24536en_US
dc.identifier.epage24570en_US
dc.identifier.doi10.18653/v1/2026.acl-long.1126en_US
dcterms.abstractAlignment techniques often inadvertently induce sycophancy in LLMs. While prior studies examined this behavior in direct-answer settings, the role of Chain-of-Thought (CoT) reasoning remains underexplored: does it serve as a logical constraint that mitigates sycophancy, or as a tool for post-hoc rationalization that masks it? We evaluate a range of models across objective and subjective tasks to investigate this issue. Results show that reasoning generally reduces sycophancy in final decisions but also masks sycophancy in some samples, where models construct deceptive justifications through logical inconsistencies, calculation errors, and one-sided arguments. Furthermore, LLMs are more prone to sycophancy in subjective tasks and under authority bias. Our mechanistic analysis on three open-source models reveals that the tendency toward sycophancy is dynamic during the reasoning process rather than predetermined at the input stage.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn 64th Annual Meeting of the Association for Computational Linguistic: Proceedings of the Conference Vol. 1 (Long Papers), p. 24536–24570. Kerrville : Association for Computational Linguistics(ACL), 2026en_US
dcterms.issued2026-
dc.relation.ispartofbookProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)en_US
dc.relation.conferenceAnnual Meeting of the Association for Computational Linguisticsen_US
dc.description.validate202607 bcwcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera4672-
dc.identifier.SubFormID53570-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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