# quqxui/Awesome-LLM4IE-Papers

Awesome papers about generative Information Extraction (IE) using Large Language Models (LLMs)

Repository: https://github.com/quqxui/Awesome-LLM4IE-Papers
Canonical: https://ross.abutalabs.com/products/awesome-llm4ie-papers
Homepage: https://arxiv.org/abs/2312.17617
License Family: other
Topics: cross-domain-learning, data-augmentation, event-arguments, event-detection, event-extraction, few-shot-learning, in-context-learning, information-extraction, knowledge-graph-construction, large-language-models, named-entity-recognition, relation-extraction, zero-shot-learning
Last push: 2024-11-18T07:52:03+00:00

## Health v2 (maintenance only)
Score: 26/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 69
- inputs: {"age_days": 979, "days_push": 653, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1060, forks 61 (observed 2026-08-28T04:03:25.715351+00:00)

## What it is
A curated awesome-list of academic papers on generative Information Extraction (IE) using Large Language Models, organized by task (NER, relation extraction, event extraction) and technique (fine-tuning, few-shot, zero-shot, data augmentation). It accompanies a peer-reviewed survey published in Frontiers of Computer Science and is regularly updated with new papers and datasets.

## Use cases
- find papers on LLM-based information extraction
- survey of large language models for named entity recognition
- research on generative relation extraction with LLMs
- papers on event extraction using large language models
- zero-shot and few-shot information extraction literature
- datasets for LLM information extraction research
- knowledge graph construction with LLMs papers
- literature review for generative IE survey

## When to choose
- you are researching LLM applications to information extraction tasks
- you need a curated, categorized reading list with paper venues and code links
- you want accompanying datasets and toolkits for IE research
- you want a peer-reviewed survey with a maintained public repository

## When to avoid
- you need runnable software or a library rather than a paper list
- you want non-generative, traditional IE methods predating LLMs
- you need production-ready IE pipelines instead of research references

## Facets
- artifact type: learning-resource
- maturity: active
- function: nlp, machine-learning, documentation
- domain: large-language-models, artificial-intelligence, tutorials, awesome-lists
- platform: -
- tags: awesome-list, information-extraction, survey, papers, named-entity-recognition, relation-extraction, event-extraction, knowledge-graph-construction, few-shot-learning, zero-shot-learning, in-context-learning, data-augmentation, natural-language-processing, web-server

## Member repositories
- quqxui/Awesome-LLM4IE-Papers (main) score 26

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:25.715351+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:57:03.643961+00:00, confidence not recorded.
  - readme: https://github.com/quqxui/Awesome-LLM4IE-Papers (fetched 2026-08-28T04:03:25.715351+00:00, sha 9b755ab09aa5)
  - homepage: https://arxiv.org/abs/2312.17617 (fetched 2026-08-29T12:59:14.084628+00:00, sha 8cad9ab432d6)
  - site_page: https://info.arxiv.org/about/donate.html (fetched 2026-08-29T12:59:14.093749+00:00, sha cca9c3a11c56)
  - site_page: https://info.arxiv.org/about/ourmembers.html (fetched 2026-08-29T12:59:14.097332+00:00, sha 47cbc55ff1de)
  - site_page: https://info.arxiv.org/about (fetched 2026-08-29T12:59:14.099387+00:00, sha a1f16f915a9a)
  - site_page: https://info.arxiv.org/labs/index.html (fetched 2026-08-29T12:59:14.095603+00:00, sha b14a8d05a0ec)
- Data as of 2026-08-30T08:39:29.467469+00:00.