Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning (2505.05237v1)

Abstract: Few-shot tabular learning, in which machine learning models are trained with a limited amount of labeled data, provides a cost-effective approach to addressing real-world challenges. The advent of LLMs has sparked interest in leveraging their pre-trained knowledge for few-shot tabular learning. Despite promising results, existing approaches either rely on test-time knowledge extraction, which introduces undesirable latency, or text-level knowledge, which leads to unreliable feature engineering. To overcome these limitations, we propose Latte, a training-time knowledge extraction framework that transfers the latent prior knowledge within LLMs to optimize a more generalized downstream model. Latte enables general knowledge-guided downstream tabular learning, facilitating the weighted fusion of information across different feature values while reducing the risk of overfitting to limited labeled data. Furthermore, Latte is compatible with existing unsupervised pre-training paradigms and effectively utilizes available unlabeled samples to overcome the performance limitations imposed by an extremely small labeled dataset. Extensive experiments on various few-shot tabular learning benchmarks demonstrate the superior performance of Latte, establishing it as a state-of-the-art approach in this domain

Transfering LLMs' Latent-level Knowledge for Few-shot Tabular Learning

The paper "Transfering LLMs' Latent-level Knowledge for Few-shot Tabular Learning" investigates the capacity of LLMs to enhance few-shot learning in tabular datasets by transfering latent-level knowledge. The research outlines an approach designed to leverage latent representations from LLMs to improve the predictive accuracy of both classification and regression tasks in tabular domains. Given the challenging nature of few-shot scenarios, which involve learning with limited labeled data, this investigation holds particular importance in the fields of machine learning and artificial intelligence.

Methodology and Dataset

The authors evaluated their proposed method using nine real-world datasets—six for classification tasks and three for regression tasks. The classification datasets include domains such as banking, healthcare, and credit evaluation, while the regression datasets cover areas like housing prices and biological metrics. The diverse nature of these chosen datasets serves to thoroughly assess the effectiveness of the proposed approach across different contexts.

Results

In the regression experiments, the methodology was tested against several baseline approaches like LogReg, XGBoost, RandomForest, SCARF, and In-context Learning. The evaluation results reveal that the proposed method consistently outperforms these baselines, especially in terms of Mean Squared Error (MSE) across the Abalone, Boston, and Cholesterol datasets. Notably, the findings indicate that direct prompting of LLMs often leads to inaccurate predictions when dealing with regression tasks, primarily due to the continuous nature of the label space which can introduce noise and exacerbate issues like model hallucination.

Discussion and Implications

This paper presents compelling evidence that latent-level knowledge transfer from LLMs can significantly enhance few-shot tabular learning, outperforming traditional machine learning models in both classification and regression domains. The authors note a distinct contrast between the performance of LLMs on regression versus classification, highlighting the challenges associated with continuous label spaces in regression contexts. These insights imply that while LLMs hold potential for tabular data tasks, the complexities of regression require further refinement to mitigate hallucinations and improve mapping accuracy.

Future Work

Given the promising results and identified challenges, future research could explore more sophisticated techniques to better handle continuous label spaces and improve LLM's numerical predictions in regression tasks. Additionally, extending the latent-level knowledge transfer method to other types of data structures or more complex applications could yield broader applications and enhance the robustness of few-shot learning models in various AI-driven domains. These advancements could potentially spur new developments in how LLMs are integrated into data science workflows, offering enriched interpretability and functionality for complex tabular data analysis.