---
title: "LLM knowledge for improved Out-of-Distribution Tabular Prediction - A Cross-Validated Regularisation Approach"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.36806v1"
permalink: "https://feed.astralor.com/items/itm_ecce0fdbda66eedd1e98b1f3"
published: "2026-10-27T08:08:51.000Z"
score: 58
channel: "tech"
tags: ["LLM","RAG","Out-of-Distribution Tabular Prediction","Cross-Validated Regularisation Approach","Prediction","Expert","LLMs"]
---

# LLM knowledge for improved Out-of-Distribution Tabular Prediction - A Cross-Validated Regularisation Approach

## 速览

Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer. Expert elicitation is slow and unscalabl…

## 关键点

- Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer.
- Expert elicitation is slow and unscalable.
- We ask whether coefficient priors elicited from large language models (LLMs) improve out-of-distribution (OOD) prediction as the shrinkage target of a regularised regression instead of zero, across five tabular datasets…

## 全文

Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer. Expert elicitation is slow and unscalable. We ask whether coefficient priors elicited from large language models (LLMs) improve out-of-distribution (OOD) prediction as the shrinkage target of a regularised regression instead of zero, across five tabular datasets split on real covariates, selecting providers and prior structures by source-domain cross-validation alone. The priors help on four of five datasets, by up to 7.9% OOD mean squared error, and ten of twenty-four prior-informed results resolve the mean improvement at conventional confidence; single-deployment intervals are much wider. On classification the gain is in probabilistic accuracy, not ranking, and control tests with corrupted priors attribute the benefit to feature-specific content rather than scale. Two findings temper this. Variation across candidate priors exceeds the average benefit, yet cross-validation margins fall below their own noise, so selection avoids the worst prior rather than finding the best. On three datasets the prior improves in-domain accuracy as much as out-of-domain, making it better regularisation than shift correction. Only where the training data are large enough that the in-domain gain disappears does elicited knowledge demonstrably improve transfer. Code, priors and results: https://github.com/hugohammer/llm-priors-ood.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_ecce0fdbda66eedd1e98b1f3