Incorporating LLM Priors into Tabular Learners

Max Zhu, Siniša Stanivuk, Andrija Petrovic, Mladen Nikolic, Pietro Lio

Keywords: Large Language Models, Few-shot tabular learners.

TL;DR: Using LLM priors to improve tabular classifiers few-shot accuracy.

Abstract: We present a method to integrate Large Language Models (LLMs) and traditional tabular data classification techniques, addressing LLMs’ challenges like data serialization sensitivity and biases. We introduce two strategies utilizing LLMs for ranking categorical variables and generating priors on correlations between continuous variables and targets, enhancing performance in few-shot scenarios. We focus on Logistic Regression, introducing MonotonicLR that employs a non-linear monotonic function for mapping ordinals to cardinals while preserving LLM-determined orders. Validation against baseline models reveals the superior performance of our approach, especially in low-data scenarios, while remaining interpretable.

Submission Number: 28