Summary of Learning to Route Llms with Confidence Tokens, by Yu-neng Chuang et al.
Learning to Route LLMs with Confidence Tokens
by Yu-Neng Chuang, Helen Zhou, Prathusha Kameswara Sarma, Parikshit Gopalan, John Boccio, Sara Bolouki, Xia Hu
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 novel study explores the reliability of large language models (LLMs) in high-stakes settings by investigating their ability to indicate confidence in answer accuracy. The proposed Self-REF training strategy introduces confidence tokens into LLMs, enabling the extraction of a confidence score. This approach is shown to significantly improve downstream routing and rejection learning tasks compared to conventional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way for large language models (LLMs) to show when they’re not sure about their answers. This is important because LLMs are used in many real-life situations, and we need them to be reliable. The scientists created a special training method called Self-REF that helps LLMs understand when they’re correct or not. They tested this approach and found it works better than other methods. |
