Summary of Rethinking Scale: the Efficacy Of Fine-tuned Open-source Llms in Large-scale Reproducible Social Science Research, by Marcello Carammia and Stefano Maria Iacus and Giuseppe Porro
Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research
by Marcello Carammia, Stefano Maria Iacus, Giuseppe Porro
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 new paper explores the use of Large Language Models (LLMs) in social science text classification tasks, highlighting the benefits and limitations of these powerful tools. The study notes that while very large, closed-source models can deliver superior performance, their use poses significant risks due to lack of transparency, potential exposure of sensitive data, challenges to replicability, and dependence on proprietary systems. Additionally, the high costs of these models make them impractical for large-scale research projects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that help sort through lots of text. Scientists use them to categorize things like news articles or social media posts. These special computers can be really good at this job, but they have some big problems too. First, you don’t always know exactly how they’re doing their work, which makes it hard to check if they’re correct. Second, these computers might accidentally share private information. Third, it’s hard to repeat the same experiment with a different computer because each one is unique. And finally, these super smart computers can be very expensive and not suitable for big research projects. |
Keywords
» Artificial intelligence » Text classification
