When Large Language Models Meet Vector Databases: A Survey

• URL: http://arxiv.org/abs/2402.01763v3
• Date: Fri, 01 Nov 2024 03:49:59 GMT
• Title: When Large Language Models Meet Vector Databases: A Survey
• Authors: Zhi Jing, Yongye Su, Yikun Han,
• Abstract summary: VecDBs offer efficient means to store, retrieve, and manage the high-dimensional vector representations intrinsic to LLM operations. VecDBs emerge as a compelling solution to these issues by offering an efficient means to store, retrieve, and manage the high-dimensional vector representations intrinsic to LLM operations. This survey aims to catalyze further research into optimizing the confluence of LLMs and VecDBs for advanced data handling and knowledge extraction capabilities.
• Score: 0.0
• License:
• Abstract: This survey explores the synergistic potential of Large Language Models (LLMs) and Vector Databases (VecDBs), a burgeoning but rapidly evolving research area. With the proliferation of LLMs comes a host of challenges, including hallucinations, outdated knowledge, prohibitive commercial application costs, and memory issues. VecDBs emerge as a compelling solution to these issues by offering an efficient means to store, retrieve, and manage the high-dimensional vector representations intrinsic to LLM operations. Through this nuanced review, we delineate the foundational principles of LLMs and VecDBs and critically analyze their integration's impact on enhancing LLM functionalities. This discourse extends into a discussion on the speculative future developments in this domain, aiming to catalyze further research into optimizing the confluence of LLMs and VecDBs for advanced data handling and knowledge extraction capabilities.

Related papers

• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• A Survey on Large Language Models with some Insights on their Capabilities and Limitations [0.3222802562733786]
  Large Language Models (LLMs) exhibit remarkable performance across various language-related tasks. LLMs have demonstrated emergent abilities extending beyond their core functions. This paper explores the foundational components, scaling mechanisms, and architectural strategies that drive these capabilities.
  arXiv  Detail & Related papers  (2025-01-03T21:04:49Z)
• Trustworthy and Efficient LLMs Meet Databases [9.243304683497904]
  Making large language models (LLMs) more trustworthy and efficient has gained significant attention. This tutorial explores such efforts and makes them transparent to the database community.
  arXiv  Detail & Related papers  (2024-12-23T22:34:40Z)
• LLM Augmentations to support Analytical Reasoning over Multiple Documents [8.99490805653946]
  We investigate the application of large language models (LLMs) to enhance in-depth analytical reasoning within the context of intelligence analysis. We develop an architecture to augment the capabilities of an LLM with a memory module called dynamic evidence trees (DETs) to develop and track multiple investigation threads.
  arXiv  Detail & Related papers  (2024-11-25T06:00:42Z)
• Enhancing Temporal Understanding in LLMs for Semi-structured Tables [50.59009084277447]
  We conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of large language models (LLMs) Our investigation leads to enhancements in TempTabQA, a dataset specifically designed for temporal temporal question answering. We introduce a novel approach, C.L.E.A.R. to strengthen LLM capabilities in this domain.
  arXiv  Detail & Related papers  (2024-07-22T20:13:10Z)
• The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective [53.48484062444108]
  We find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs; on the other hand, MLLMs can facilitate the development of data. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective.
  arXiv  Detail & Related papers  (2024-07-11T15:08:11Z)
• Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges [47.45993726498343]
  Data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection. This survey explores the transformative impact of large language models (LLMs) on DA, particularly addressing the unique challenges and opportunities they present in the context of natural language processing (NLP) and beyond.
  arXiv  Detail & Related papers  (2024-03-05T14:11:54Z)
• 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)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback [127.75419038610455]
  Large language models (LLMs) are able to generate human-like, fluent responses for many downstream tasks. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules.
  arXiv  Detail & Related papers  (2023-02-24T18:48:43Z)

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.