Summary of Red-ct: a Systems Design Methodology For Using Llm-labeled Data to Train and Deploy Edge Classifiers For Computational Social Science, by David Farr et al.
RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Science
by David Farr, Nico Manzonelli, Iain Cruickshank, Jevin West
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 proposed study adopts a systems design approach to utilize large language models (LLMs) as imperfect data annotators for downstream supervised learning tasks. The methodology aims to improve classification performance by introducing novel system intervention measures. Experimental results show that the approach outperforms LLM-generated labels in seven of eight tests, making it an effective strategy for incorporating LLMs into industry use cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study uses big language models to help with tasks like classifying information. However, there are concerns about how much these models cost, how they work with computer networks, and how secure they are. The researchers came up with a new way to use these models that makes them better at their job. They tested this approach and found that it worked well in most cases. |
Keywords
* Artificial intelligence * Classification * Supervised
