title: Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for
    Political Text
summary: Social scientists quickly adopted large language models due to their ability
  to annotate documents without supervised training, an ability known as
  zero-shot learning. However, due to their compute demands, cost, and often
  proprietary nature, these models are often at odds with replication and open
  science standards. This paper introduces the Political DEBATE (DeBERTa
  Algorithm for Textual Entailment) language models for zero-shot and few-shot
  classification of political documents. These models are not only as good, or
  better than, state-of-the art large language models at zero and few-shot
  classification, but are orders of magnitude more efficient and completely open
  source. By training the models on a simple random sample of 10-25 documents,
  they can outperform supervised classifiers trained on hundreds or thousands of
  documents and state-of-the-art generative models with complex, engineered
  prompts. Additionally, we release the PolNLI dataset used to train these models
  -- a corpus of over 200,000 political documents with highly accurate labels
  across over 800 classification tasks.
paper_authors._id: 66d969ff36aa505569569194
paper_authors.name: Michael Burnham
paper_authors._id: 66d969ff36aa505569569195
paper_authors.name: Kayla Kahn
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-05T08:55:42.160Z
paper_authors._id: 66902066a4d0df7e518d9bf2
paper_authors.avatarUrl: /avatars/d4973a2693b146e756847d081709b792.svg
paper_authors.fullname: Kayla Kahn
paper_authors.type: user
paper_authors.user: kaylakahn
paper_authors._id: 66d969ff36aa505569569196
paper_authors.name: Ryan Yank Wang
paper_authors._id: 66d969ff36aa505569569197
paper_authors.name: Rachel X. Peng
paper_title: Political DEBATE: Efficient Zero-shot and Few-shot Classifiers for
    Political Text
paper_summary: Social scientists quickly adopted large language models due to their ability
  to annotate documents without supervised training, an ability known as
  zero-shot learning. However, due to their compute demands, cost, and often
  proprietary nature, these models are often at odds with replication and open
  science standards. This paper introduces the Political DEBATE (DeBERTa
  Algorithm for Textual Entailment) language models for zero-shot and few-shot
  classification of political documents. These models are not only as good, or
  better than, state-of-the art large language models at zero and few-shot
  classification, but are orders of magnitude more efficient and completely open
  source. By training the models on a simple random sample of 10-25 documents,
  they can outperform supervised classifiers trained on hundreds or thousands of
  documents and state-of-the-art generative models with complex, engineered
  prompts. Additionally, we release the PolNLI dataset used to train these models
  -- a corpus of over 200,000 political documents with highly accurate labels
  across over 800 classification tasks.
paper_ai_keywords: zero-shot learning few-shot learning DeBERTa Political DEBATE Textual Entailment politcal documents state-of-the-art large language models few-shot classification supervised classifiers generative models complex prompts PolNLI dataset corpus of political documents classification tasks