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L2G-SR: Learning to Guide LLM-based Symbolic Regression

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Briefings

L2G-SR is a new framework for LLM-based symbolic regression that uses a learned structural verifier based on tree edit distance (TED) to guide search. Unlike traditional methods relying solely on numerical error, L2G-SR aligns numerical observations with formulas and scores candidates based on structural similarity. On LLM-SRBench, it achieved state-of-the-art numerical fit, equation level accuracy, and TED across various LLM backbones and domains.

Key points

• L2G-SR uses tree edit distance (TED) to construct a structural supervision signal that accounts for commutative operators and alternative expression forms.
• The framework guides search by aligning numerical observations with formulas before selecting parent candidates, complementing numerical error in the process.

Context

Symbolic regression searches for equations explaining data, but numerical error alone can favor accurate surrogates over structurally correct candidates. L2G-SR addresses this by using a learned structural verifier to guide search with a learned structural verifier.

Limitations

Evidence is limited to a single preprint on arXiv; results are not yet validated on external datasets or real-world applications. The study uses LLM-SRBench metrics and does not report computational cost or training time.

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