Abstract
Stance detection enables the inference of attitudes from human communications. Automatic stance identification was mostly cast as a classification problem. However, stance decisions involve complex judgments, which can be nowadays generated by prompting Large Language Models (LLMs). In this paper we present a new method for stance identification which (1) relies on a new prompting framework, called Tree-of-Counterfactual prompting; (2) operates not only on textual communications, but also on images; (3) allows more than one stance object type; and (4) requires no examples of stance attribution, thus it is a “Tabula Rasa” Zero-Shot Stance Detection (TR-ZSSD) method. Our experiments indicate surprisingly promising results, outperforming fine-tuned stance detection systems.- Anthology ID:
- 2024.acl-long.49
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 861–880
- Language:
- URL:
- https://aclanthology.org/2024.acl-long.49
- DOI:
- Cite (ACL):
- Maxwell Weinzierl and Sanda Harabagiu. 2024. Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 861–880, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection (Weinzierl & Harabagiu, ACL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.49.pdf