Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications

• URL: http://arxiv.org/abs/2311.05876v3
• Date: Wed, 23 Oct 2024 14:48:20 GMT
• Title: Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
• Authors: Zhangyin Feng, Weitao Ma, Weijiang Yu, Lei Huang, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting liu,
• Abstract summary: Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. We propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
• Score: 41.24492058141363
• License:
• Abstract: Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursued two primary strategies, knowledge editing and retrieval augmentation, to enhance LLMs by incorporating external information from different aspects. Nevertheless, there is still a notable absence of a comprehensive survey. In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications. In addition, we conduct an in-depth analysis of different methods and point out potential research directions in the future. We hope this survey offers the community quick access and a comprehensive overview of this research area, with the intention of inspiring future research endeavors.

Related papers

• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
  Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence. This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
  arXiv  Detail & Related papers  (2024-09-21T15:22:26Z)
• Surveying the MLLM Landscape: A Meta-Review of Current Surveys [17.372501468675303]
  Multimodal Large Language Models (MLLMs) have become a transformative force in the field of artificial intelligence. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs.
  arXiv  Detail & Related papers  (2024-09-17T14:35:38Z)
• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers [81.47046536073682]
  We present a review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature. We hope our work can provide the community with quick access and spur breakthrough research in MLLMs.
  arXiv  Detail & Related papers  (2024-04-07T11:52:44Z)
• Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey [17.19337964440007]
  There is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This survey aims to address this gap by consolidating recent progress in these areas, offering a thorough survey and taxonomy of the datasets, metrics, and methodologies utilized. It identifies strengths, limitations, unexplored territories, and gaps in the existing literature, while providing some insights for future research directions in this vital and rapidly evolving field.
  arXiv  Detail & Related papers  (2024-02-27T23:59:01Z)
• The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey [6.516561905186376]
  The advent of Large Language Models (LLMs) represents a notable breakthrough in Natural Language Processing (NLP) We study the inherent challenges associated with extending context length and present an organized overview of the existing strategies employed by researchers. We explore whether there is a consensus within the research community regarding evaluation standards and identify areas where further agreement is needed.
  arXiv  Detail & Related papers  (2024-01-15T18:07:21Z)
• Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques. We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
  arXiv  Detail & Related papers  (2023-12-29T14:25:22Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• Multimodal Research in Vision and Language: A Review of Current and Emerging Trends [41.07256031348454]
  We present a detailed overview of the latest trends in research pertaining to visual and language modalities. We look at its applications in their task formulations and how to solve various problems related to semantic perception and content generation. We shed some light on multi-disciplinary patterns and insights that have emerged in the recent past, directing this field towards more modular and transparent intelligent systems.
  arXiv  Detail & Related papers  (2020-10-19T13:55:10Z)

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.