Trends in Integration of Knowledge and Large Language Models: A Survey
and Taxonomy of Methods, Benchmarks, and Applications
- URL: http://arxiv.org/abs/2311.05876v2
- Date: Thu, 7 Dec 2023 12:42:07 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(LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations.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: 42.61727038213399
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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language tasks, but they are susceptible to issues stemming from outdated data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 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) - A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation.
Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge.
RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
arXiv Detail & Related papers (2024-05-10T02:48:45Z) - RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing [0.2302001830524133]
This survey paper addresses the absence of a comprehensive overview on Retrieval-Augmented Language Models (RALMs)
The paper discusses the essential components of RALMs, including Retrievers, Language Models, and Augmentations.
RALMs demonstrate utility in a spectrum of tasks, from translation and dialogue systems to knowledge-intensive applications.
arXiv Detail & Related papers (2024-04-30T13:14:51Z) - 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]
Information extraction aims to extract structural knowledge from plain natural language texts.
generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation.
LLMs offer viable solutions for IE tasks based on a generative paradigm.
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.