Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study (2024.lrec-main)
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| Challenge: | Existing models for stance detection are not robust enough to be used in a viewpoint-diverse news recommender because the news constantly has new discussion topics. |
| Approach: | They propose to use two stance task definitions (Pro/Con versus Same Side Stance) and two LLM architectures (bi-encoding versus cross-encode) to test model performance. |
| Outcome: | The proposed models outperform the same side-stance definition and other models on stance across different topics. |
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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: | Existing methods for stance detection are topic-specific and cross-target stance. |
| Approach: | They propose a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. |
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Are Stereotypes Leading LLMs’ Zero-Shot Stance Detection ? (2025.emnlp-main)
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| Challenge: | Large Language Models inherit stereotypes from their pretraining data, leading to biased behavior toward certain social groups in many tasks. |
| Approach: | They propose to annotate posts in pre-existing stance detection datasets with dialect or vernacular of a specific group and text complexity/readability to investigate whether these attributes influence the model’s stance detect decisions. |
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| Challenge: | stance detection is a task to identify attitudes from opinions towards certain targets, but it is expensive and time-consuming . stance detector is based on labeled data, but unlabeled data can be collected easier . |
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Zero-Shot and Few-Shot Stance Detection on Varied Topics via Conditional Generation (2023.acl-short)
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| Challenge: | Existing work on stance detection focuses on in-domain or leave-out targets with only a few target choices. |
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| Challenge: | Current approaches to fact-checking are time-consuming and tedious. |
| Approach: | They propose a novel approach which leverages labeled data in one language to identify relative perspective of a document with respect to a claim in a different target language. |
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Modeling Human-Like Cognition for Stance Detection: Integrating Intuitive Judgment and Analytical Reasoning (2026.acl-long)
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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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TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings (2023.emnlp-main)
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| Challenge: | Recent studies have focused on topic-specific stance classifiers that fail to generalize to unseen topics. |
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Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning (2024.lrec-main)
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| Challenge: | Stance Reasoner is a model for zero-shot stance detection on social media platforms that can be used to extract opinions from opinionated content. |
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A Challenge Dataset and Effective Models for Conversational Stance Detection (2024.lrec-main)
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| Challenge: | stance detection studies focus on evaluating stances within individual instances, hindering progress of conversational stance analysis. |
| Approach: | They propose a multi-turn conversation stance detection dataset that encompasses multiple targets for conversational stance detector. |
| Outcome: | The proposed dataset encompasses multiple targets for conversational stance detection. |