Political Debate with LLMs
So essentially,
Political DEBATE models can Political DEBATE
Paper: Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for Political Text (26 Pages)
Researchers from Princeton University, Pennsylvania State University, Louisiana State University are proposing two new language models, Political DEBATE Large and Political DEBATE Base, which are designed for zero-shot and few-shot classification of political text.
Hmm..What’s the background?
Unlike many LLMs, the Political DEBATE models are completely open source, meaning researchers can access and modify the model architecture and training data. The authors also committed to versioning the models and datasets for replication purposes, addressing concerns about the transparency and reproducibility of research using LLMs.
The Political DEBATE models are significantly smaller than many state-of-the-art LLMs, with 86 million and 304 million parameters, respectively, compared to the tens of billions of parameters in models like Claude 3.5 Sonnet. This smaller size makes them more efficient to train and deploy, even on consumer-grade hardware.
Ok, So what is proposed in the research paper?
The authors argue that the widespread use of proprietary LLMs in social science research, while convenient, raises concerns about reproducibility and open science standards due to their closed nature, large compute requirements, and cost.
The authors present PolNLI, a new dataset of over 200,000 political documents with high-quality labels across a wide range of political science sub-fields, specifically designed to train and evaluate their models.
The authors adopt a Natural Language Inference (NLI) framework, which they believe offers a good balance between efficiency and flexibility for zero-shot and few-shot classification tasks in the political domain.
What’s next?
The authors suggest exploring the application of these models to tasks beyond stance, topic, hate-speech, and event classification. This could include:
Entity Recognition: Identifying key entities (people, organizations, locations) in political texts
Relationship Extraction: Determining the relationships between identified entities (e.g., who is affiliated with which organization, what actions were taken by whom)
Better and comprehensive benchmarking
So essentially,
Political DEBATE models can Political DEBATE
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