rLLM (relationLLM): A PyTorch Library Designed for Relational Table Learning (RTL) with Large Language Models (LLMs)

Practical Solutions for Relational Table Learning with Large Language Models (LLMs)

Challenges in Real-World Application of LLMs

Large language models (LLMs) have shown remarkable text understanding and generation capabilities in artificial intelligence. However, their application to real-world big data poses significant challenges due to high costs. The rLLM project addresses these challenges by providing a platform for rapid development of relational table learning (RTL) methods using LLMs.

The rLLM Project: Key Functions and Applications

The rLLM project focuses on decomposing state-of-the-art Graph Neural Networks (GNNs), LLMs, and Table Neural Networks (TNNs) into standardized modules. It introduces a simple RTL method called BRIDGE to process table data and establish relationships between table samples using GNNs. Additionally, the project introduces a robust data collection named SJTUTables to address the scarcity of datasets in the emerging field of RTL.

Comprehensive Architecture of the rLLM Project

The rLLM project introduces a comprehensive architecture consisting of three main layers: the Data Engine Layer, the Module Layer, and the Model Layer. This structure is designed to facilitate efficient processing and analysis of relational table data.

Superior Capabilities of the BRIDGE Algorithm

Experimental results reveal that the BRIDGE algorithm demonstrates superior capabilities in processing relational table data by effectively combining a table encoder with a graph encoder. It achieves a significant performance improvement over conventional methods, highlighting the importance of considering the relational structure of data in table learning tasks.

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