Large Language Models are Good Relational Learners

• URL: http://arxiv.org/abs/2506.05725v1
• Date: Fri, 06 Jun 2025 04:07:55 GMT
• Title: Large Language Models are Good Relational Learners
• Authors: Fang Wu, Vijay Prakash Dwivedi, Jure Leskovec,
• Abstract summary: We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
• Score: 55.40941576497973
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated remarkable capabilities across various domains, yet their application to relational deep learning (RDL) remains underexplored. Existing approaches adapt LLMs by traversing relational links between entities in a database and converting the structured data into flat text documents. Still, this text-based serialization disregards critical relational structures, introduces redundancy, and often exceeds standard LLM context lengths. We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for LLMs within a retrieval-augmented generation (RAG) framework. Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to effectively process and reason over complex entity relationships. Specifically, the GNN encoder extracts a local subgraph around an entity to build feature representations that contain relevant entity relationships and temporal dependencies. These representations are transformed into structured prompts using a denormalization process, effectively allowing the LLM to reason over relational structures. Through extensive experiments, we demonstrate that Rel-LLM outperforms existing methods on key RDL tasks, offering a scalable and efficient approach to integrating LLMs with structured data sources. Code is available at https://github.com/smiles724/Rel-LLM.

Related papers

• SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation [63.4105693174085]
  We propose textbfSelfRACG, a novel paradigm that enables large language models (LLMs) to express their information needs to enhance textbfRACG.<n>SelfRACG includes an information need expression module and a two-stage information need-guided training strategy, which encourages LLMs to express their information need.<n>Extensive experiments demonstrate that SelfRACG can retrieve external knowledge that better aligns with the LLM's own information needs, resulting in superior generation performance compared to vanilla RACG.
  arXiv  Detail & Related papers  (2025-07-25T07:42:01Z)
• SAFT: Structure-Aware Fine-Tuning of LLMs for AMR-to-Text Generation [50.277959544420455]
  SAFT is a structure-aware fine-tuning approach that injects graph topology into pretrained language models.<n>We compute direction-sensitive positional encodings from the magnetic Laplacian of transformed AMRs.<n> SAFT sets a new state-of-the-art on AMR 3.0 with a 3.5 BLEU improvement over baselines.
  arXiv  Detail & Related papers  (2025-07-15T18:12:57Z)
• Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB [44.057784044659726]
  Large language models (LLMs) have made it easier to prototype such retrieval and reasoning data pipelines.<n>This often involves orchestrating data systems, managing data movement, and handling low-level details.<n>We introduce FlockMTL: an extension for abstractions that integrates deeply LLM capabilities and retrieval-augmented generation.
  arXiv  Detail & Related papers  (2025-04-01T19:48:17Z)
• LLM with Relation Classifier for Document-Level Relation Extraction [25.587850398830252]
  Large language models (LLMs) have created a new paradigm for natural language processing.<n>This paper investigates the causes of this performance gap, identifying the dispersion of attention by LLMs due to entity pairs without relations as a key factor.
  arXiv  Detail & Related papers  (2024-08-25T16:43:19Z)
• Beyond Inter-Item Relations: Dynamic Adaption for Enhancing LLM-Based Sequential Recommendation [83.87767101732351]
  Sequential recommender systems (SRS) predict the next items that users may prefer based on user historical interaction sequences. Inspired by the rise of large language models (LLMs) in various AI applications, there is a surge of work on LLM-based SRS. We propose DARec, a sequential recommendation model built on top of coarse-grained adaption for capturing inter-item relations.
  arXiv  Detail & Related papers  (2024-08-14T10:03:40Z)
• Struct-X: Enhancing Large Language Models Reasoning with Structured Data [38.558614152006975]
  Struct-X operates through five key phases: read-model-fill-reflect-reason'' It encodes structured data into a topological space using graph embeddings. It fills in missing entity information with knowledge retrieval modules. The final phase involves constructing a topological network with selected tokens.
  arXiv  Detail & Related papers  (2024-07-17T13:06:25Z)
• Text-like Encoding of Collaborative Information in Large Language Models for Recommendation [58.87865271693269]
  We introduce BinLLM, a novel method to seamlessly integrate collaborative information with Large Language Models for Recommendation (LLMRec) BinLLM converts collaborative embeddings from external models into binary sequences. BinLLM provides options to compress the binary sequence using dot-decimal notation to avoid excessively long lengths.
  arXiv  Detail & Related papers  (2024-06-05T12:45:25Z)
• CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model [1.9950682531209156]
  Domain-Specific Chinese Relation Extraction (DSCRE) aims to extract relations between entities from domain-specific Chinese text. Given the impressive performance of large language models (LLMs) in natural language processing, we propose a new framework called CRE-LLM.
  arXiv  Detail & Related papers  (2024-04-28T06:27:15Z)
• Self-Retrieval: End-to-End Information Retrieval with One Large Language Model [97.71181484082663]
  We introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture. Self-Retrieval internalizes the retrieval corpus through self-supervised learning, transforms the retrieval process into sequential passage generation, and performs relevance assessment for reranking.
  arXiv  Detail & Related papers  (2024-02-23T18:45:35Z)
• Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text [44.81698187939784]
  This paper introduces Structure Guided Prompt, a framework designed to improve the multi-step reasoning capabilities of Large Language Models (LLMs) Our experiments show that this framework significantly enhances the reasoning capabilities of LLMs, enabling them to excel in a broader spectrum of natural language scenarios.
  arXiv  Detail & Related papers  (2024-02-20T22:56:23Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.