Summary of Uncertainty-based Abstention in Llms Improves Safety and Reduces Hallucinations, by Christian Tomani et al.
Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations
by Christian Tomani, Kamalika Chaudhuri, Ivan Evtimov, Daniel Cremers, Mark Ibrahim
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study explores the feasibility of abstaining from answering uncertain questions in large language models (LLMs) for improving their reliability. It addresses three key situations where LLMs currently lack reliability: correctness, hallucinations on unanswerable questions, and safety. Inspired by classification approaches, the authors investigate two types of uncertainty metrics: statistical and verbalized measures, called In-Dialogue Uncertainty (InDU). By combining these measures with models using Reinforcement Learning with Human Feedback (RLHF), the study shows that abstaining based on the right uncertainty measure can boost LLM reliability. The results demonstrate improved correctness by 2% to 8%, reduced hallucinations by 50%, and increased safety by 70% up to 99%. This approach requires only a few highly uncertain samples, resulting in almost no additional computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can make large language models better. Right now, these models are not very reliable because they sometimes give wrong answers or pretend they know things they don’t. The study finds that if we teach the models to say “I’m not sure” when they’re unsure, this can actually improve their accuracy and prevent them from making mistakes. This approach also helps reduce the number of times the model makes something up (called hallucinations). By doing so, the models become safer and more reliable. |
Keywords
» Artificial intelligence » Classification » Reinforcement learning » Rlhf
