@inproceedings{56983,
  abstract     = {{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.}},
  author       = {{Qudus, Umair and Röder, Michael and Vollmers, Daniel and Ngonga Ngomo, Axel-Cyrille}},
  booktitle    = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management}},
  isbn         = {{79-8-4007-0436-9/24/10}},
  keywords     = {{Stance Classification, Few-shot in-context learning, Pre-trained large language models}},
  location     = {{Boise, ID, USA}},
  pages        = {{3994 -- 3999}},
  publisher    = {{ACM}},
  title        = {{{ExPrompt: Augmenting Prompts Using Examples as Modern Baseline for Stance Classification}}},
  doi          = {{10.1145/3627673.3679923}},
  volume       = {{9}},
  year         = {{2024}},
}