Related papers: AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM

AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM

URL: http://arxiv.org/abs/2510.17934v1
Date: Mon, 20 Oct 2025 15:40:14 GMT
Title: AtlasKV: Augmenting LLMs with Billion-Scale Knowledge Graphs in 20GB VRAM
Authors: Haoyu Huang, Hong Ting Tsang, Jiaxin Bai, Xi Peng, Gong Zhang, Yangqiu Song,
Abstract summary: We propose a scalable, effective, and general way to augment large language models with billion-scale knowledge graphs.<n> KG2KV and HiKVP integrate KG triples into LLMs at scale with sub-linear time and memory complexity.<n>It maintains strong knowledge grounding and generalization performance using the LLMs' inherent attention mechanism.
Score: 60.583864162256525
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Retrieval-augmented generation (RAG) has shown some success in augmenting large language models (LLMs) with external knowledge. However, as a non-parametric knowledge integration paradigm for LLMs, RAG methods heavily rely on external retrieval modules and the retrieved textual context prior. Especially for very large scale knowledge augmentation, they would introduce substantial inference latency due to expensive searches and much longer relevant context. In this paper, we propose a parametric knowledge integration method, called \textbf{AtlasKV}, a scalable, effective, and general way to augment LLMs with billion-scale knowledge graphs (KGs) (e.g. 1B triples) using very little GPU memory cost (e.g. less than 20GB VRAM). In AtlasKV, we introduce KG2KV and HiKVP to integrate KG triples into LLMs at scale with sub-linear time and memory complexity. It maintains strong knowledge grounding and generalization performance using the LLMs' inherent attention mechanism, and requires no external retrievers, long context priors, or retraining when adapting to new knowledge.

Related papers

KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing [64.38243807002878]
We present KnowTrace, an elegant RAG framework to mitigate the context overload in large language models.<n>KnowTrace autonomously traces out desired knowledge triplets to organize a specific knowledge graph relevant to the input question.<n>It consistently surpasses existing methods across three multi-hop question answering benchmarks.
arXiv Detail & Related papers (2025-05-26T17:22:20Z)
Prompting is not Enough: Exploring Knowledge Integration and Controllable Generation on Large Language Models [89.65955788873532]
Open-domain question answering (OpenQA) represents a cornerstone in natural language processing (NLP)<n>We propose a novel framework named GenKI, which aims to improve the OpenQA performance by exploring Knowledge Integration and controllable Generation.
arXiv Detail & Related papers (2025-05-26T08:18:33Z)
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.<n> Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs.<n>We propose a novel Lightweight and efficient Prompt learning-ReasOning Framework for KGQA (LightPROF)
arXiv Detail & Related papers (2025-04-04T03:03:47Z)
Parametric Retrieval Augmented Generation [32.29608109539912]
Parametric RAG is a new RAG paradigm that integrates external knowledge directly into the parameters of feed-forward networks.<n>It substantially enhances both the effectiveness and efficiency of knowledge augmentation in large language models.
arXiv Detail & Related papers (2025-01-27T10:04:49Z)
Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation [81.18701211912779]
We introduce an Adaptive Multi-Aspect Retrieval-augmented over KGs (Amar) framework.<n>This method retrieves knowledge including entities, relations, and subgraphs, and converts each piece of retrieved text into prompt embeddings.<n>Our method has achieved state-of-the-art performance on two common datasets.
arXiv Detail & Related papers (2024-12-24T16:38:04Z)
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)
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)
Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context [4.1229332722825]
This paper introduces a novel framework that combines graph-driven context retrieval in conjunction to knowledge graphs based enhancement. We conduct experiments on various Large Language Models (LLMs) with different parameter sizes to evaluate their ability to ground knowledge and determine factual accuracy in answers to open-ended questions. Our methodology GraphContextGen consistently outperforms dominant text-based retrieval systems, demonstrating its robustness and adaptability to a larger number of use cases.
arXiv Detail & Related papers (2024-01-23T11:25:34Z)

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