Summary of Paired Completion: Flexible Quantification Of Issue-framing at Scale with Llms, by Simon D Angus and Lachlan O’neill
Paired Completion: Flexible Quantification of Issue-framing at Scale with LLMs
by Simon D Angus, Lachlan O’Neill
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel application of generative large language models (LLMs) is developed to detect issue framing and narrative analysis within large text datasets. The method, called “paired completion,” reliably detects issue framing with only a few examples of either perspective on a given issue. This is achieved by introducing next-token log probabilities derived from LLMs. The paired completion method is evaluated against prompt-based LLM methods and labelled methods using traditional NLP and recent LLM contextual embeddings. The results show that paired completion outperforms other methods in detecting issue framing. Additionally, the study conducts a cost-based analysis to mark out the feasible set of performant methods at production-level scales and a model bias analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Issue framing is a crucial concept in understanding how people think about certain topics. This research develops new ways to detect issue framing in large amounts of text data. The method uses machine learning models that learn from patterns in language to identify different perspectives on an issue. The study shows that this method can accurately identify issue framing even when the words used by different sides are very similar. This is important for people who want to analyze and understand large amounts of text data, such as social scientists, policy analysts, and program evaluators. |
Keywords
» Artificial intelligence » Machine learning » Nlp » Prompt » Token
