新闻
Know When the Equation Is Wrong: Structural Uncertainty for LLM-Guided Symbolic Regression under Extrapolation
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers introduced LLM-SR-UQ, a framework that quantifies uncertainty in LLM-discovered equations by tracking disagreement among competing mathematical forms.
- 为何重要
- Engineers building symbolic regression systems need this when extrapolating beyond training data, where different equations fit equally well but diverge in predictions.
- 注意
- The method extends conformal prediction to structural uncertainty but has only been tested on LLM-SRBench dataset; real-world generalization remains unproven.
- llm
- language model
- rag
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- Uncertainty Quantification
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