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Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs

Simon Angus and Lachlan O'Neill

Papers from arXiv.org

Abstract: 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.

Date: 2024-08, Revised 2025-06
New Economics Papers: this item is included in nep-big
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