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    <abstract>Stance detection (SD) identifies a text’s position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as input. We evaluate Large Language Models (LLMs) from GPT, Gemini, Llama, and Mistral families, comparing their performance to the only existing work, Target-Stance Extraction (TSE), which benefits from predefined targets. Unlike TSE, OTSD removes the dependency of a predefined list, making target generation and evaluation more challenging. We also provide a metric for evaluating target quality that correlates well with human judgment. Our experiments reveal that LLMs outperform TSE in target generation, both when the real target is explicitly and not explicitly mentioned in the text. Similarly, LLMs overall surpass TSE in stance detection for both explicit and non-explicit cases. However, LLMs struggle in both target generation and stance detection when the target is not explicit.</abstract>
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%0 Conference Proceedings
%T Can Large Language Models Address Open-Target Stance Detection?
%A Akash, Abu Ubaida
%A Fahmy, Ahmed
%A Trabelsi, Amine
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F akash-etal-2025-large
%X Stance detection (SD) identifies a text’s position towards a target, typically labeled as favor, against, or none. We introduce Open-Target Stance Detection (OTSD), the most realistic task where targets are neither seen during training nor provided as input. We evaluate Large Language Models (LLMs) from GPT, Gemini, Llama, and Mistral families, comparing their performance to the only existing work, Target-Stance Extraction (TSE), which benefits from predefined targets. Unlike TSE, OTSD removes the dependency of a predefined list, making target generation and evaluation more challenging. We also provide a metric for evaluating target quality that correlates well with human judgment. Our experiments reveal that LLMs outperform TSE in target generation, both when the real target is explicitly and not explicitly mentioned in the text. Similarly, LLMs overall surpass TSE in stance detection for both explicit and non-explicit cases. However, LLMs struggle in both target generation and stance detection when the target is not explicit.
%R 10.18653/v1/2025.findings-acl.54
%U https://aclanthology.org/2025.findings-acl.54/
%U https://doi.org/10.18653/v1/2025.findings-acl.54
%P 971-985

Markdown (Informal)

[Can Large Language Models Address Open-Target Stance Detection?](https://aclanthology.org/2025.findings-acl.54/) (Akash et al., Findings 2025)

• Can Large Language Models Address Open-Target Stance Detection? (Akash et al., Findings 2025)

ACL

• Abu Ubaida Akash, Ahmed Fahmy, and Amine Trabelsi. 2025. Can Large Language Models Address Open-Target Stance Detection?. In Findings of the Association for Computational Linguistics: ACL 2025, pages 971–985, Vienna, Austria. Association for Computational Linguistics.