Summary of Llm Chain Ensembles For Scalable and Accurate Data Annotation, by David Farr et al.
LLM Chain Ensembles for Scalable and Accurate Data Annotation
by David Farr, Nico Manzonelli, Iain Cruickshank, Kate Starbird, Jevin West
First submitted to arxiv on: 16 Oct 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 This paper proposes an innovative approach to deploy large language models (LLMs) efficiently for zero-shot classification tasks. By chaining multiple LLMs together, the authors create a system that routes data subsets to subsequent models based on their uncertainty in classification. This allows each model to focus on instances where it is most confident, while more complex cases are forwarded to potentially more robust models. The results demonstrate that this chain ensemble method often outperforms individual models and achieves significant cost savings, making it a practical solution for large-scale data annotation challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how big language models can help with a big problem in data science. When we need lots of labeled data to train AI models, but getting that data is hard or expensive, these models can be very useful. The authors came up with a new way to use multiple language models together, so each model only does what it’s good at and passes tricky cases to the next one. This makes the whole process more efficient and accurate, which could help us solve real-world problems. |
Keywords
* Artificial intelligence * Classification * Zero shot
