新闻
Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers propose Divergent Token Confidence, a method that estimates LLM reasoning uncertainty by counting tokens where two models strongly disagree during decoding.
- 为何重要
- Matters for engineers building systems where LLM confidence calibration affects decision-making, especially in mathematical reasoning and high-stakes applications.
- 注意
- Paper is under peer review and not yet published. Method requires an auxiliary model for comparison, adding computational overhead in practical deployment.
- llm
- language model
- reasoning
- token
- eval
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