New LLM framework quantifies uncertainty in symbolic regression

Researchers have developed a new framework called LLM-SR-UQ to address the challenge of uncertainty in large language model-guided symbolic regression. Existing methods often provide a single best equation without quantifying uncertainty, which is problematic as different equations can fit data similarly but extrapolate differently. LLM-SR-UQ retains competing equation structures and uses their disagreement to characterize uncertainty, improving coverage attainment, especially under extrapolation, while maintaining in-distribution coverage. AI

IMPACT Improves the reliability of LLM-generated mathematical models by quantifying uncertainty, crucial for scientific discovery and extrapolation.

RANK_REASON The cluster describes a new framework and methodology presented in an academic paper, focusing on a specific problem within symbolic regression. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• LLM-SRBench
• LLM-SR-UQ
• ScienceCast

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →