Summary of Poliprompt: a High-performance Cost-effective Llm-based Text Classification Framework For Political Science, by Menglin Liu et al.
PoliPrompt: A High-Performance Cost-Effective LLM-Based Text Classification Framework for Political Science
by Menglin Liu, Ge Shi
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper introduces a three-stage in-context learning approach that leverages large language models (LLMs) to improve text classification efficiency in political science. The authors’ method incorporates automatic enhanced prompt generation, adaptive exemplar selection, and a consensus mechanism that resolves discrepancies between two weaker LLMs, refined by an advanced LLM. This approach surpasses traditional machine learning methods that often require extensive feature engineering, human labeling, and task-specific training. The paper validates its approach using datasets from the BBC news reports, Kavanaugh Supreme Court confirmation, and 2018 election campaign ads, demonstrating significant improvements in classification F1 score (+0.36 for zero-shot classification) with manageable economic costs (-78% compared with human labeling). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers better at understanding text from political science. It uses big language models that are really good at learning from small amounts of data. The authors tried this method on some examples and it worked really well! They were able to get the computer to make accurate decisions with much less effort than before. This is important because it could help us understand more about what’s happening in politics without having to spend a lot of time and money. |
Keywords
» Artificial intelligence » Classification » F1 score » Feature engineering » Machine learning » Prompt » Text classification » Zero shot
