Reasoner Outperforms: Generative Stance Detection with Rationalization for Social Media
Stance detection is vital for promoting a trustworthy, human-centric Web by identifying biased or harmful narratives in user-generated content. Whereas recent LLM-based methods excel in accuracy, they often lack interpretability. We propose a generative stance detection approach that outputs explicit rationales and distills them into smaller language models (SLMs) via single-task and multitask lea- doi
- 10.1145/3720553.3746668
- isbn
- 979-8-4007-1534-1
- name
- Reasoner Outperforms: Generative Stance Detection with Rationalization for Social Media
- pages
- 28–32
- source
- supplied BITS XML + supplied PDF
- acm_url
- https://dl.acm.org/doi/10.1145/3720553.3746668
- authors
- Jiaqing Yuan, Ruijie Xi, Munindar P Singh
- doi_url
- https://doi.org/10.1145/3720553.3746668
- license
- © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.
- summary
- Stance detection is vital for promoting a trustworthy, human-centric Web by identifying biased or harmful narratives in user-generated content. Whereas recent LLM-based methods excel in accuracy, they often lack interpretability. We propose a generative stance detection approach that outputs explicit rationales and distills them into smaller language models (SLMs) via single-task and multitask lea
- keywords
- Social Media, Generative AI, Argumentation, Rationales
- arxiv_url
- https://arxiv.org/abs/2412.10266
- published
- 2025-09-15
- conference
- HT '25: 36th ACM Conference on Hypertext and Social Media, Chicago, IL, USA, September 15–19, 2025
- open_access
- false
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- https://dl.acm.org/doi/full/10.1145/3720553.3746668
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- (empty)
- displayAuthor
- Jiaqing Yuan, Ruijie Xi, Munindar P Singh
- proceedings_url
- https://dl.acm.org/doi/proceedings/10.1145/3720553
- displayPublishTime
- 2025-09-15
- acm_reference_format
- (empty)
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