[{"project":[{"name":"NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen","_id":"412"}],"quality_controlled":"1","citation":{"ieee":"U. Qudus, M. Röder, D. Vollmers, and A.-C. Ngonga Ngomo, “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification,” in <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management</i>, Boise, ID, USA, 2024, vol. 9, pp. 3994–3999, doi: <a href=\"https://doi.org/10.1145/3627673.3679923\">10.1145/3627673.3679923</a>.","apa":"Qudus, U., Röder, M., Vollmers, D., &#38; Ngonga Ngomo, A.-C. (2024). ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification. <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management</i>, <i>9</i>, 3994–3999. <a href=\"https://doi.org/10.1145/3627673.3679923\">https://doi.org/10.1145/3627673.3679923</a>","chicago":"Qudus, Umair, Michael Röder, Daniel Vollmers, and Axel-Cyrille Ngonga Ngomo. “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification.” In <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management</i>, 9:3994–99. ACM, 2024. <a href=\"https://doi.org/10.1145/3627673.3679923\">https://doi.org/10.1145/3627673.3679923</a>.","short":"U. Qudus, M. Röder, D. Vollmers, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, ACM, 2024, pp. 3994–3999.","mla":"Qudus, Umair, et al. “ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification.” <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management</i>, vol. 9, ACM, 2024, pp. 3994–99, doi:<a href=\"https://doi.org/10.1145/3627673.3679923\">10.1145/3627673.3679923</a>.","bibtex":"@inproceedings{Qudus_Röder_Vollmers_Ngonga Ngomo_2024, title={ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification}, volume={9}, DOI={<a href=\"https://doi.org/10.1145/3627673.3679923\">10.1145/3627673.3679923</a>}, booktitle={Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}, publisher={ACM}, author={Qudus, Umair and Röder, Michael and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}, year={2024}, pages={3994–3999} }","ama":"Qudus U, Röder M, Vollmers D, Ngonga Ngomo A-C. ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification. In: <i>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management</i>. Vol 9. ACM; 2024:3994-3999. doi:<a href=\"https://doi.org/10.1145/3627673.3679923\">10.1145/3627673.3679923</a>"},"popular_science":"1","file_date_updated":"2024-11-11T13:24:19Z","has_accepted_license":"1","conference":{"end_date":"2024-10-25","start_date":"2024-10-21","name":"CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","location":"Boise, ID, USA"},"status":"public","volume":9,"ddc":["006"],"user_id":"83392","_id":"56983","publisher":"ACM","page":"3994 - 3999","abstract":[{"lang":"eng","text":"Detecting the veracity of a statement automatically is a challenge the world is grappling with due to the vast amount of data spread across the web. Verifying a given claim typically entails validating it within the framework of supporting evidence like a retrieved piece of text. Classifying the stance of the text with respect to the claim is called stance classification. Despite advancements in automated fact-checking, most systems still rely on a substantial quantity of labeled training data, which can be costly. In this work, we avoid the costly training or fine-tuning of models by reusing pre-trained large language models together with few-shot in-context learning. Since we do not train any model, our approach ExPrompt is lightweight, demands fewer resources than other stance classification methods and can serve as a modern baseline for future developments. At the same time, our evaluation shows that our approach is able to outperform former state-of-the-art stance classification approaches regarding accuracy by at least 2 percent. Our scripts and data used in this paper are available at https://github.com/dice-group/ExPrompt."}],"publication":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","keyword":["Stance Classification","Few-shot in-context learning","Pre-trained large language models"],"type":"conference","date_created":"2024-11-11T13:15:25Z","file":[{"date_updated":"2024-11-11T13:24:19Z","relation":"main_file","access_level":"closed","file_size":531579,"file_name":"public.pdf","content_type":"application/pdf","success":1,"file_id":"56984","creator":"uqudus","date_created":"2024-11-11T13:24:19Z"}],"intvolume":"         9","date_updated":"2025-09-11T09:49:07Z","publication_status":"published","author":[{"id":"83392","full_name":"Qudus, Umair","last_name":"Qudus","orcid":"0000-0001-6714-8729","first_name":"Umair"},{"id":"67199","orcid":"https://orcid.org/0000-0002-8609-8277","first_name":"Michael","last_name":"Röder","full_name":"Röder, Michael"},{"full_name":"Vollmers, Daniel","first_name":"Daniel","last_name":"Vollmers"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","id":"65716"}],"publication_identifier":{"isbn":["79-8-4007-0436-9/24/10"]},"title":"ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification","year":"2024","doi":"10.1145/3627673.3679923","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://dl.acm.org/doi/10.1145/3627673.3679923"}]}]