Bridging LLMs and KGs without Fine-Tuning: Intermediate Probing Meets Subgraph-Aware Entity Descriptions

• URL: http://arxiv.org/abs/2408.06787v4
• Date: Tue, 05 Aug 2025 13:56:40 GMT
• Title: Bridging LLMs and KGs without Fine-Tuning: Intermediate Probing Meets Subgraph-Aware Entity Descriptions
• Authors: Bo Xue, Yi Xu, Yunchong Song, Jiaxin Ding, Luoyi Fu, Xinbing Wang,
• Abstract summary: Large Language Models (LLMs) encapsulate extensive world knowledge and exhibit powerful context modeling capabilities.<n>We propose a novel framework that synergizes the strengths of LLMs with robust knowledge representation to enable effective and efficient KGC.<n>We achieve a 47% relative improvement over previous methods based on non-fine-tuned LLMs and, to our knowledge, are the first to achieve classification performance comparable to fine-tuned LLMs.
• Score: 49.36683223327633
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traditional knowledge graph completion (KGC) methods rely solely on structural information, struggling with the inherent sparsity of knowledge graphs (KGs). By contrast, Large Language Models (LLMs) encapsulate extensive world knowledge and exhibit powerful context modeling capabilities, making them promising for mitigating the limitations of traditional methods. However, direct fine-tuning of LLMs for KGC, though effective, imposes substantial computational and memory overheads, while utilizing non-fine-tuned LLMs is efficient but yields suboptimal performance. In this work, we propose a novel framework that synergizes the strengths of LLMs with robust knowledge representation to enable effective and efficient KGC. We extract the context-aware hidden states of knowledge triples from the intermediate layers of LLMs, thereby capturing rich semantic and relational nuances. These representations are then utilized to train a data-efficient classifier tailored specifically for KGC tasks. To bridge the semantic gaps between LLMs and KGs, we employ subgraph sampling on KGs to generate model-friendly entity descriptions. We further adopt sliced mutual information (SMI) as a principled metric to quantify the task-specific information encoded in these representations. Extensive experiments on standard benchmarks validate the efficiency and effectiveness of our approach. We achieve a 47\% relative improvement over previous methods based on non-fine-tuned LLMs and, to our knowledge, are the first to achieve classification performance comparable to fine-tuned LLMs while enhancing GPU memory efficiency by $188\times$ and accelerating training and inference by $26.11\times$.

Related papers

• Ontology-Enhanced Knowledge Graph Completion using Large Language Models [20.080012331845065]
  Large Language Models (LLMs) have been extensively adopted in Knowledge Graph Completion (KGC)<n>We propose an enhanced KGC method using LLMs -- OL-KGC.<n>It first leverages neural perceptual mechanisms to effectively embed structural information into the textual space, and then uses an automated extraction algorithm to retrieve ontological knowledge.
  arXiv  Detail & Related papers  (2025-07-28T09:00:48Z)
• IAM: Efficient Inference through Attention Mapping between Different-scale LLMs [74.81417160018856]
  IAM framework achieves dual benefits of accelerated attention computation and reduced KV cache usage.<n>We show that IAM can accelerate prefill by 15% and reduce KV cache usage by 22.1% without appreciably sacrificing performance.
  arXiv  Detail & Related papers  (2025-07-16T06:39:11Z)
• Injecting Knowledge Graphs into Large Language Models [0.0]
  We build on encoding techniques which integrate graph embeddings within the Large Language Models as tokens.<n>Our approach is model-agnostic, resource-efficient, and compatible with any LLMs.
  arXiv  Detail & Related papers  (2025-05-12T13:31:26Z)
• Does Knowledge Distillation Matter for Large Language Model based Bundle Generation? [13.491190612749534]
  Knowledge distillation offers a promising solution, transferring expertise from large teacher models to compact student models. This study systematically investigates knowledge distillation approaches for bundle generation, aiming to minimize computational demands while preserving performance. We propose a comprehensive KD framework that (i) progressively extracts knowledge (patterns, rules, deep thoughts); (ii) captures varying quantities of distilled knowledge through different strategies; and (iii) exploits complementary LLM adaptation techniques for domain-specific adaptation and enhanced efficiency.
  arXiv  Detail & Related papers  (2025-04-24T03:18:16Z)
• LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph [57.382255728234064]
  Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs. We propose a novel Lightweight and efficient Prompt learning-ReasOning Framework for KGQA (LightPROF)
  arXiv  Detail & Related papers  (2025-04-04T03:03:47Z)
• LLM is Knowledge Graph Reasoner: LLM's Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation [47.34949656215159]
  Large Language Models (LLMs) can be considered databases with a wealth of knowledge learned from the web data. We propose a LLM's Intuition-aware Knowledge graph Reasoning model (LIKR) Our model outperforms state-of-the-art recommendation methods in cold-start sequential recommendation scenarios.
  arXiv  Detail & Related papers  (2024-12-17T01:52:15Z)
• KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning [74.21524111840652]
  This paper proposes textbfKaLM, a textitKnowledge-aligned Language Modeling approach. It fine-tunes autoregressive large language models to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment. Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks.
  arXiv  Detail & Related papers  (2024-12-06T11:08:24Z)
• Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [9.844598565914055]
  Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge.<n>We introduce SubgraphRAG, extending the Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) framework that retrieves subgraphs.<n>Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval.
  arXiv  Detail & Related papers  (2024-10-28T04:39:32Z)
• Search for Efficient Large Language Models [52.98684997131108]
  Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Weight pruning, quantization, and distillation have been embraced to compress LLMs, targeting memory reduction and inference acceleration. Most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
  arXiv  Detail & Related papers  (2024-09-25T21:32:12Z)
• DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System [83.34921966305804]
  Large language models (LLMs) have demonstrated remarkable performance in recommender systems.<n>We propose a novel plug-and-play alignment framework for LLMs and collaborative models.<n>Our method is superior to existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-08-15T15:56:23Z)
• Traditional Methods Outperform Generative LLMs at Forecasting Credit Ratings [17.109522466982476]
  Large Language Models (LLMs) have been shown to perform well for many downstream tasks. This paper investigates how well LLMs perform in the task of forecasting corporate credit ratings.
  arXiv  Detail & Related papers  (2024-07-24T20:30:55Z)
• All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks [51.19110891434727]
  Large Language Models (LLMs) with pretrained knowledge and powerful semantic comprehension abilities have recently shown a remarkable ability to benefit applications using vision and text data. E-LLaGNN is a framework with an on-demand LLM service that enriches message passing procedure of graph learning by enhancing a limited fraction of nodes from the graph.
  arXiv  Detail & Related papers  (2024-07-20T22:09:42Z)
• Large Language Models as Reliable Knowledge Bases? [60.25969380388974]
  Large Language Models (LLMs) can be viewed as potential knowledge bases (KBs) This study defines criteria that a reliable LLM-as-KB should meet, focusing on factuality and consistency. strategies like ICL and fine-tuning are unsuccessful at making LLMs better KBs.
  arXiv  Detail & Related papers  (2024-07-18T15:20:18Z)
• On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models [33.08049246893537]
  Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) We propose a simple but effective long-tail knowledge detection method for LLMs. Our method achieves over 4x speedup in average inference time and consistent performance improvement in downstream tasks.
  arXiv  Detail & Related papers  (2024-06-24T07:17:59Z)
• LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text Classification [13.319594321038926]
  We propose a simple and effective transfer learning strategy, namely LLMEmbed, to address this classical but challenging task. We perform extensive experiments on publicly available datasets, and the results show that LLMEmbed achieves strong performance while enjoys low training overhead.
  arXiv  Detail & Related papers  (2024-06-06T03:46:59Z)
• Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback [5.778012023739487]
  We propose Knowledge Graph Tuning (KGT) to personalize large language models (LLMs) KGT extracts personalized factual knowledge triples from users' queries and feedback and optimize KGs without modifying the LLM parameters. Experiments with state-of-the-art LLMs, including GPT-2, Llama2, and Llama3, show that KGT significantly improves personalization performance while reducing latency and GPU memory costs.
  arXiv  Detail & Related papers  (2024-05-30T04:57:03Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• Automated Commit Message Generation with Large Language Models: An Empirical Study and Beyond [24.151927600694066]
  Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs. This paper conducts the first comprehensive experiment to investigate how far we have been in applying Large Language Models (LLMs) to generate high-quality commit messages.
  arXiv  Detail & Related papers  (2024-04-23T08:24:43Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Large Language Models Can Better Understand Knowledge Graphs Than We Thought [13.336418752729987]
  knowledge graph (KG) embeddings with model parameters become increasingly costly. Current prompting methods often rely on a trial-and-error approach. We show that unordered linearized triples are more effective for LLMs' understanding of KGs compared to fluent NL text.
  arXiv  Detail & Related papers  (2024-02-18T10:44:03Z)
• Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion [24.545917737620197]
  Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps. This paper aims to provide a comprehensive perspective on harnessing the advantages of Large Language Models for reasoning in temporal knowledge graphs.
  arXiv  Detail & Related papers  (2024-01-11T17:42:47Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)

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.