Detecting issue framing in text - how different perspectives approach the same topic - is valuable for social science and policy analysis, yet challenging for automated methods due to subtle linguistic differences. We introduce `paired completion', a novel approach using LLM next-token log probabilities to detect contrasting frames using minimal examples. Through extensive evaluation across synthetic datasets and a human-labeled corpus, we demonstrate that paired completion is a cost-efficient, low-bias alternative to both prompt-based and embedding-based methods, offering a scalable solution for analyzing issue framing in large text collections, especially suited to low-resource settings.
@article{arxiv.2408.09742,
title = {Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs},
author = {Simon D Angus and Lachlan O'Neill},
journal= {arXiv preprint arXiv:2408.09742},
year = {2025}
}