Summary of Large Language Models Must Be Taught to Know What They Don’t Know, by Sanyam Kapoor et al.
Large Language Models Must Be Taught to Know What They Don’t Know
by Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This paper addresses the crucial issue of trusting predictions from large language models (LLMs) in high-stakes applications. While some studies suggest that prompting high-performance LLMs is sufficient to produce calibrated uncertainties, others propose sampling methods that can be computationally expensive. The authors argue that prompting alone is insufficient and instead introduce a fine-tuning approach using a small dataset of correct and incorrect answers. They demonstrate that this method achieves good calibration with generalization and low computational overhead, outperforming baseline methods with as few as 1,000 graded examples. Additionally, the study investigates the mechanisms enabling reliable LLM uncertainty estimation, finding that many models can serve as general-purpose uncertainty estimators. Furthermore, they show that these estimates inform human use of LLMs in human-AI collaborative settings through a user study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how we can trust predictions from large language models (LLMs) when making important decisions. Some people think that just asking the model to make good guesses is enough, while others suggest ways to improve these guesses. The researchers argue that simply asking the model isn’t enough and instead propose a way to fine-tune the model using only a few correct and incorrect answers. They show that this approach works well and can be done quickly, even with just 1,000 examples. The study also looks at why this method works and finds that many models can help estimate uncertainty for other models too. Finally, they demonstrate how these uncertainty estimates can help people work better with AI in collaborative settings. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Prompting
