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        <title>Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection</title>
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            <namePart type="given">Vivek</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-long.49</identifier>
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%0 Conference Proceedings
%T Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection
%A Weinzierl, Maxwell
%A Harabagiu, Sanda
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F weinzierl-harabagiu-2024-tree
%X 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.
%R 10.18653/v1/2024.acl-long.49
%U https://aclanthology.org/2024.luhme-long.49/
%U https://doi.org/10.18653/v1/2024.acl-long.49
%P 861-880

Markdown (Informal)

[Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection](https://aclanthology.org/2024.luhme-long.49/) (Weinzierl & Harabagiu, ACL 2024)

• Tree-of-Counterfactual Prompting for Zero-Shot Stance Detection (Weinzierl & Harabagiu, ACL 2024)

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.