LLMs Should Express Uncertainty Explicitly
📰 ArXiv cs.AI
LLMs should be trained to express uncertainty explicitly to improve decision-making in applications like abstention and verification
Action Steps
- Train LLMs to verbalize calibrated confidence scores
- Implement a global interface for uncertainty expression
- Develop a local interface for uncertainty expression at the token level
- Evaluate the effectiveness of explicit uncertainty expression in downstream tasks
Who Needs to Know This
AI engineers and researchers benefit from this approach as it enables more transparent and controllable models, while product managers can utilize this feature to make more informed decisions
Key Insight
💡 Explicit uncertainty expression can improve the transparency and controllability of LLMs
Share This
💡 LLMs should express uncertainty explicitly to drive better decisions
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