Can Large Language Models Address Open-Target Stance Detection? (2025.findings-acl)
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| Challenge: | Stance detection (SD) identifies a text’s position towards a target, typically labeled as favor, against, or none. |
| Approach: | They introduce Open-Target Stance Detection (OTSD) which aims to determine the position of a text towards a target, typically labeled as favor, against, or none. |
| Outcome: | The proposed model outperforms the only existing task, Target-Stance Extraction (TSE), which benefits from predefined targets. |
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| Challenge: | Existing methods for stance detection focus on background information and not on the accompanying input texts. |
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| Challenge: | Recent advances in large language models (LLMs) have revolutionized stance detection, enabling complex reasoning strategies such as chain-of-thought prompting. |
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| Challenge: | Stance detection is a task that focuses on the classification of a writer’s viewpoint towards a target. |
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| Challenge: | Until recently, zero-shot stance detection was limited to in-domain tasks. |
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