Large Language Models for Generative Information Extraction: A Survey

• URL: http://arxiv.org/abs/2312.17617v3
• Date: Thu, 31 Oct 2024 07:23:09 GMT
• Title: Large Language Models for Generative Information Extraction: A Survey
• Authors: Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang, Enhong Chen,
• Abstract summary: 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.
• Score: 89.71273968283616
• License:
• Abstract: Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a generative paradigm. To conduct a comprehensive systematic review and exploration of LLM efforts for IE tasks, in this study, we survey the most recent advancements in this field. We first present an extensive overview by categorizing these works in terms of various IE subtasks and techniques, and then we empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs. Based on a thorough review conducted, we identify several insights in technique and promising research directions that deserve further exploration in future studies. We maintain a public repository and consistently update related works and resources on GitHub (\href{https://github.com/quqxui/Awesome-LLM4IE-Papers}{LLM4IE repository})

Related papers

• A Survey on Large Language Models for Code Generation [9.555952109820392]
  Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks. This survey aims to bridge the gap between academia and practical development by providing a comprehensive and up-to-date literature review.
  arXiv  Detail & Related papers  (2024-06-01T17:48:15Z)
• LLMs Meet Multimodal Generation and Editing: A Survey [89.76691959033323]
  This survey elaborates on multimodal generation and editing across various domains, comprising image, video, 3D, and audio. We summarize the notable advancements with milestone works in these fields and categorize these studies into LLM-based and CLIP/T5-based methods. We dig into tool-augmented multimodal agents that can leverage existing generative models for human-computer interaction.
  arXiv  Detail & Related papers  (2024-05-29T17:59:20Z)
• Tool Learning with Large Language Models: A Survey [60.733557487886635]
  Tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization.
  arXiv  Detail & Related papers  (2024-05-28T08:01:26Z)
• 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)
• 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)
• Advances in Embodied Navigation Using Large Language Models: A Survey [16.8165925743264]
  The article offers an exhaustive summary of the symbiosis between Large Language Models and Embodied Intelligence. It reviews state-of-the-art models, research methodologies, and assesses the advantages and disadvantages of existing embodied navigation models and datasets. Finally, the article elucidates the role of LLMs in embodied intelligence, based on current research, and forecasts future directions in the field.
  arXiv  Detail & Related papers  (2023-11-01T14:08:56Z)
• Universal Information Extraction with Meta-Pretrained Self-Retrieval [39.69130086395689]
  Universal Information Extraction(Universal IE) aims to solve different extraction tasks in a uniform text-to-structure generation manner. Retrieving knowledge from external knowledge bases may help models to overcome this problem but it is impossible to construct a knowledge base suitable for various IE tasks. We propose MetaRetriever to retrieve task-specific knowledge from PLMs to enhance universal IE.
  arXiv  Detail & Related papers  (2023-06-18T00:16:00Z)
• Active Retrieval Augmented Generation [123.68874416084499]
  Augmenting large language models (LMs) by retrieving information from external knowledge resources is one promising solution. Most existing retrieval augmented LMs employ a retrieve-and-generate setup that only retrieves information once based on the input. We propose Forward-Looking Active REtrieval augmented generation (FLARE), a generic method which iteratively uses a prediction of the upcoming sentence to anticipate future content.
  arXiv  Detail & Related papers  (2023-05-11T17:13:40Z)

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.