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

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{angus2025_2408.09742,
  title={ Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs },
  author={ Simon D Angus and Lachlan OÑeill },
  journal={arXiv preprint arXiv:2408.09742},
  year={ 2025 }
}