[{"file":[{"success":1,"content_type":"application/pdf","file_id":"56984","date_updated":"2024-11-11T13:24:19Z","relation":"main_file","access_level":"closed","file_size":531579,"file_name":"public.pdf","date_created":"2024-11-11T13:24:19Z","creator":"uqudus"}],"date_created":"2024-11-11T13:15:25Z","type":"conference","keyword":["Stance Classification","Few-shot in-context learning","Pre-trained large language models"],"publication":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","abstract":[{"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.","lang":"eng"}],"main_file_link":[{"url":"https://dl.acm.org/doi/10.1145/3627673.3679923"}],"language":[{"iso":"eng"}],"doi":"10.1145/3627673.3679923","year":"2024","title":"ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification","author":[{"orcid":"0000-0001-6714-8729","last_name":"Qudus","first_name":"Umair","full_name":"Qudus, Umair","id":"83392"},{"orcid":"https://orcid.org/0000-0002-8609-8277","first_name":"Michael","last_name":"Röder","full_name":"Röder, Michael","id":"67199"},{"full_name":"Vollmers, Daniel","first_name":"Daniel","last_name":"Vollmers"},{"first_name":"Axel-Cyrille","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","id":"65716"}],"publication_identifier":{"isbn":["79-8-4007-0436-9/24/10"]},"date_updated":"2025-09-11T09:49:07Z","publication_status":"published","intvolume":"         9","popular_science":"1","file_date_updated":"2024-11-11T13:24:19Z","citation":{"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>","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>.","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.","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>"},"quality_controlled":"1","project":[{"name":"NEBULA: Nutzerzentrierte KI-basierte Erkennung von Fake-News und Fehlinformationen","_id":"412"}],"page":"3994 - 3999","_id":"56983","publisher":"ACM","ddc":["006"],"user_id":"83392","volume":9,"status":"public","conference":{"location":"Boise, ID, USA","name":"CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management","start_date":"2024-10-21","end_date":"2024-10-25"},"has_accepted_license":"1"}]