Summary of Prompt Selection Matters: Enhancing Text Annotations For Social Sciences with Large Language Models, by Louis Abraham et al.
Prompt Selection Matters: Enhancing Text Annotations for Social Sciences with Large Language Models
by Louis Abraham, Charles Arnal, Antoine Marie
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 The abstract proposes an investigation into the influence of prompt selection on text annotation tasks, specifically in social sciences. Large Language Models (LLMs) have been applied to these tasks with impressive results, rivaling or surpassing human performance at a fraction of the cost. However, it remains unclear how different prompts impact labeling accuracy. The study aims to shed light on this issue by examining the effects of various prompts and developing an automatic prompt optimization method to create high-quality prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are being used for text annotation tasks in social sciences with great success. They can do these jobs just as well as humans, but much cheaper. But nobody has looked into how choosing different prompts affects the results. This study wants to change that by showing how different prompts affect labeling accuracy and developing a way to create the best prompts automatically. |
Keywords
» Artificial intelligence » Optimization » Prompt
