{"id":28676527,"url":"https://github.com/zjunlp/low-resource-kepapers","last_synced_at":"2026-01-31T12:02:11.058Z","repository":{"id":59397359,"uuid":"453359836","full_name":"zjunlp/Low-resource-KEPapers","owner":"zjunlp","description":"A Paper List of Low-resource Information Extraction","archived":false,"fork":false,"pushed_at":"2024-11-16T15:55:34.000Z","size":210,"stargazers_count":130,"open_issues_count":0,"forks_count":11,"subscribers_count":6,"default_branch":"main","last_synced_at":"2025-06-13T23:04:59.951Z","etag":null,"topics":["artificial-intelligence","awsome-list","event-extraction","few-shot-learning","information-extraction","knowledge-extraction","knowledge-graph","low-resource","ner","nlp","paper","paper-list","relation-extraction","survey"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/zjunlp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-01-29T10:01:20.000Z","updated_at":"2025-04-24T06:28:08.000Z","dependencies_parsed_at":"2024-11-16T16:27:20.071Z","dependency_job_id":"b60945c9-ca9f-4130-95cf-27c537338497","html_url":"https://github.com/zjunlp/Low-resource-KEPapers","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/zjunlp/Low-resource-KEPapers","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zjunlp%2FLow-resource-KEPapers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zjunlp%2FLow-resource-KEPapers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zjunlp%2FLow-resource-KEPapers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zjunlp%2FLow-resource-KEPapers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/zjunlp","download_url":"https://codeload.github.com/zjunlp/Low-resource-KEPapers/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/zjunlp%2FLow-resource-KEPapers/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28941921,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-31T11:39:38.044Z","status":"ssl_error","status_checked_at":"2026-01-31T11:39:27.765Z","response_time":128,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","awsome-list","event-extraction","few-shot-learning","information-extraction","knowledge-extraction","knowledge-graph","low-resource","ner","nlp","paper","paper-list","relation-extraction","survey"],"created_at":"2025-06-13T23:04:59.979Z","updated_at":"2026-01-31T12:02:11.040Z","avatar_url":"https://github.com/zjunlp.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Low-resource Information Extraction 🚀\n\n🍎 The repository is a paper set on low-resource information extraction (IE), mainly including NER, RE and EE, which is generally categorized into two paradigms:  \n\n- **Traditional** Low-Resource IE approaches\n    - Exploiting Higher-Resource Data\n    - Developing Stronger Data-Efficient Models\n    - Optimizing Data and Models Together\n- **LLM-based** Low-Resource IE approaches\n    - Direct Inference Without Tuning\n    - Model Specialization With Tuning\n\n🤗 We strongly encourage the researchers who want to promote their fantastic work for the community to make pull request and update their papers in this repository! \n\n📖 **Survey Paper**: Information Extraction in Low-Resource Scenarios: Survey and Perspective (ICKG 2024) \\[[paper](https://arxiv.org/abs/2202.08063)\\]\n\n🗂️ **Slides**: \n\n- Data-Efficient Knowledge Graph Construction, 高效知识图谱构建 ([Tutorial on CCKS 2022](http://sigkg.cn/ccks2022/?page_id=24)) \\[[slides](https://drive.google.com/drive/folders/1xqeREw3dSiw-Y1rxLDx77r0hGUvHnuuE)\\] \n- Efficient and Robust Knowledge Graph Construction ([Tutorial on AACL-IJCNLP 2022](https://www.aacl2022.org/Program/tutorials)) \\[[paper](https://aclanthology.org/2022.aacl-tutorials.1.pdf), [slides](https://github.com/NLP-Tutorials/AACL-IJCNLP2022-KGC-Tutorial)\\] \n- Open-Environment Knowledge Graph Construction and Reasoning: Challenges, Approaches, and Opportunities ([Tutorial on IJCAI 2023](https://ijcai-23.org/tutorials/))  \\[[slides](https://openkg-tutorial.github.io/)\\]\n\n\n\n## Content\n\n[**Preliminaries**](#Preliminaries)\n\n* [**🛠️ Low-Resource IE Toolkits**](#%EF%B8%8F-Low-Resource-IE-Toolkits)\n  * [Traditional Toolkits](#Traditional-Toolkits)\n  * [LLM-Based Toolkits](#LLM-Based-Toolkits)\n* [**📊 Low-Resource IE Datasets**](#-Low-Resource-IE-Datasets)\n  * [Low-Resource NER](#Low-Resource-NER)\n  * [Low-Resource RE](#Low-Resource-RE)\n  * [Low-Resource EE](#Low-Resource-EE)\n* [**📖 Related Surveys/Analysis on Low-Resource IE**](#-Related-Surveys-and-Analysis-on-Low-Resource-IE)\n  * [Information Extraction](#Information-Extraction)\n  * [Low-Resource NLP Learning](#Low-Resource-NLP-Learning)\n\n\n[**🍎Traditional Methods🍎**](#-Traditional-Methods-)\n\n* [**1. Exploiting Higher-Resource Data**](#1-Exploiting-Higher-Resource-Data)\n  * [1.1 Weakly Supervised Augmentation](#Weakly-Supervised-Augmentation)\n  * [1.2 Multimodal Augmentation](#Multimodal-Augmentation)\n  * [1.3 Multi-Lingual Augmentation](#Multi-Lingual-Augmentation)\n  * [1.4 Auxiliary Knowledge Enhancement](#Auxiliary-Knowledge-Enhancement)\n* [**2. Developing Stronger Data-Efficient Models**](#2-Developing-Stronger-Data-Efficient-Models)\n  * [2.1 Meta Learning](#Meta-Learning)\n  * [2.2 Transfer Learning](#Transfer-Learning)\n  * [2.3 Fine-Tuning PLM](#Fine-Tuning-PLM)\n* [**3. Optimizing Data and Models Together**](#3-Optimizing-Data-and-Models-Together)\n  * [3.1 Multi-Task Learning](#Multi-Task-Learning)\n  * [3.2 Task Reformulation](#Task-Reformulation)\n  * [3.3 Prompt-Tuning PLM](#Prompt-Tuning-PLM)\n\n\n[**🍏LLM-Based Methods🍏**](#-LLM-Based-Methods-)\n  \n* [**Direct Inference Without Tuning**](#Direct-Inference-Without-Tuning)\n  * [Instruction Prompting](#Instruction-Prompting)\n  * [Code Prompting](#Code-Prompting)\n  * [In-Context Learning](#In-Context-Learning)\n* [**Model Specialization With Tuning**](#Model-Specialization-With-Tuning)\n  * [Prompt-Tuning LLM](#Prompt-Tuning-LLM) \n  * [Fine-Tuning LLM](#Fine-Tuning-LLM)\n\n[**How to Cite**](#How-to-Cite)\n\n\u003c!--  * [Fine-Tuning LLM](#Fine-Tuning-LLM) --\u003e\n\u003c!--  * [Retrieval-Augmented Prompting](#Retrieval-Augmented-Prompting)--\u003e\n\n\n## Preliminaries\n\n## 🛠️ Low-Resource IE Toolkits\n\n### Traditional Toolkits\n- DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population [[paper](https://aclanthology.org/2022.emnlp-demos.10/), [project](https://github.com/zjunlp/DeepKE)]\n- OpenUE: An Open Toolkit of Universal Extraction from Text [[paper](https://aclanthology.org/2020.emnlp-demos.1.pdf), [project](https://github.com/zjunlp/OpenUE)]\n- Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction [[paper](https://aclanthology.org/2023.acl-demo.34/), [project](https://github.com/IBM/zshot)]\n- OpenNRE [[paper](https://aclanthology.org/D19-3029.pdf), [project](https://github.com/thunlp/OpenNRE)]\n- OmniEvent [[paper1](https://aclanthology.org/2023.emnlp-demo.46.pdf), [paper2](https://aclanthology.org/2023.findings-acl.586.pdf), [project](https://github.com/THU-KEG/OmniEvent)]\n\n### LLM-Based Toolkits\n\u003c!--- CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction [[paper](https://arxiv.org/abs/2307.00769), [project](https://github.com/cocacola-lab/CollabKG)]--\u003e\n- CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction [[paper](https://arxiv.org/abs/2307.00769)]\n- GPT4IE [[project](https://github.com/cocacola-lab/GPT4IE)]\n- ChatIE [[paper](https://arxiv.org/abs/2302.10205), [project](https://github.com/cocacola-lab/ChatIE)]\n- TechGPT: Technology-Oriented Generative Pretrained Transformer [[project](https://github.com/neukg/TechGPT)] \n- TechGPT-2.0: A Large Language Model Project to Solve the Task of Knowledge Graph Construction [[paper](https://arxiv.org/abs/2401.04507), [project](https://github.com/neukg/TechGPT-2.0)] \n- AutoKG: LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities [[paper](https://arxiv.org/abs/2305.13168), [project](https://github.com/zjunlp/AutoKG)]\n- KnowLM [[project](https://github.com/zjunlp/KnowLM)] \n\n\n## 📊 Low-Resource IE Datasets\n\n### Low-Resource NER\n* {***Few-NERD***}: Few-NERD: A Few-shot Named Entity Recognition Dataset (EMNLP 2021) \\[[paper](https://aclanthology.org/2021.acl-long.248.pdf), [data](https://ningding97.github.io/fewnerd/)\\]\n\n### Low-Resource RE\n* {***FewRel***}: FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (EMNLP 2018) \\[[paper](https://aclanthology.org/D18-1514.pdf), [data](https://github.com/thunlp/FewRel)\\]\n* {***FewRel2.0***}: FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (EMNLP 2019) \\[[paper](https://aclanthology.org/D19-1649.pdf), [data](https://github.com/thunlp/FewRel)\\]\n* {***Wiki-ZSL***}: ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (NAACL 2021) \\[[paper](https://aclanthology.org/2021.naacl-main.272.pdf), [data](https://github.com/dinobby/ZS-BERT)\\]\n* {***Entail-RE***}: Low-resource Extraction with Knowledge-aware Pairwise Prototype Learning (Knowledge-Based Systems, 2022) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0950705121008467), [data](https://github.com/231sm/Reasoning_In_KE)\\]\n* {***LREBench***}: Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (EMNLP 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-emnlp.29.pdf), [data](https://github.com/zjunlp/LREBench)\\]\n\n### Low-Resource EE\n* {***FewEvent***}: Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection (WSDM 2020) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3336191.3371796), [data](https://github.com/231sm/Low_Resource_KBP)\\]\n* {***Causal-EE***}: Low-resource Extraction with Knowledge-aware Pairwise Prototype Learning (Knowledge-Based Systems, 2022) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0950705121008467), [data](https://github.com/231sm/Reasoning_In_KE)\\]\n* {***OntoEvent***}: OntoED: Low-resource Event Detection with Ontology Embedding (ACL 2021) \\[[paper](https://aclanthology.org/2021.acl-long.220.pdf), [data](https://github.com/231sm/Reasoning_In_EE)\\]\n* {***FewDocAE***}: Few-Shot Document-Level Event Argument Extraction (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.446.pdf), [data](https://github.com/Xianjun-Yang/FewDocAE)\\]\n\n\n## 📖 Related Surveys and Analysis on Low-Resource IE\n\n### Information Extraction\n#### NER\n* A Survey on Recent Advances in Named Entity Recognition from Deep Learning Models (COLING 2018) \\[[paper](https://aclanthology.org/C18-1182.pdf)\\]\n* A Survey on Deep Learning for Named Entity Recognition (TKDE, 2020) \\[[paper](https://ieeexplore.ieee.org/abstract/document/9039685)\\]\n* Few-Shot Named Entity Recognition: An Empirical Baseline Study (EMNLP 2021) \\[[paper](https://aclanthology.org/2021.emnlp-main.813.pdf)\\]\n* Few-shot Named Entity Recognition: definition, taxonomy and research directions (TIST, 2023) \\[[paper](https://dl.acm.org/doi/10.1145/3609483)\\]\n* Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2309.14084)\\]\n* A Survey on Recent Advances in Named Entity Recognition (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2401.10825)\\]\n\n#### RE\n* A Survey on Neural Relation Extraction (Science China Technological Sciences, 2020) \\[[paper](https://link.springer.com/article/10.1007/s11431-020-1673-6)\\]\n* Relation Extraction: A Brief Survey on Deep Neural Network Based Methods (ICSIM 2021) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3451471.3451506)\\]\n* Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes (TACL, 2021) \\[[paper](https://aclanthology.org/2021.tacl-1.42.pdf)\\]\n* Deep Neural Network-Based Relation Extraction: An Overview (Neural Computing and Applications, 2022) \\[[paper](https://link.springer.com/article/10.1007/s00521-021-06667-3)\\]\n* Revisiting Relation Extraction in the era of Large Language Models (ACL 2023) [[paper](https://aclanthology.org/2023.acl-long.868.pdf)\\]\n\n#### EE\n* A Survey of Event Extraction From Text (ACCESS, 2019) \\[[paper](https://ieeexplore.ieee.org/document/8918013)\\]\n* What is Event Knowledge Graph: A Survey (TKDE, 2022) \\[[paper](https://ieeexplore.ieee.org/abstract/document/9792280)\\]\n* A Survey on Deep Learning Event Extraction: Approaches and Applications (TNNLS, 2022) \\[[paper](https://ieeexplore.ieee.org/abstract/document/9927311)\\]\n* Event Extraction: A Survey (2022) [[paper](https://arxiv.org/abs/2210.03419)\\]\n* Low Resource Event Extraction: A Survey (2022) [[paper](https://www.cs.uoregon.edu/Reports/AREA-202210-Lai.pdf)\\]\n* Few-shot Event Detection: An Empirical Study and a Unified View (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.628.pdf)\\]\n* Exploring the Feasibility of ChatGPT for Event Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2303.03836)\\]\n* A Reevaluation of Event Extraction: Past, Present, and Future Challenges (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.09562)\\]\n* ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Refinement (arXiv, 2023) [[paper](https://arxiv.org/abs/2401.13218)\\]\n\u003c!--* Low Resource Event Extraction: A Survey (2022) [[paper](https://www.cs.uoregon.edu/Reports/AREA-202210-Lai.pdf)\\]--\u003e\n\n#### General IE\n**Traditional IE** \n\n* From Information to Knowledge: Harvesting Entities and Relationships from Web Sources (PODS 2010)  \\[[paper](https://dl.acm.org/doi/abs/10.1145/1807085.1807097)\\]\n* Knowledge Base Population: Successful Approaches and Challenges (ACL 2011) \\[[paper](https://aclanthology.org/P11-1115.pdf)\\]\n* Advances in Automated Knowledge Base Construction (NAACL-HLC 2012, AKBC-WEKEX workshop) \\[[paper](https://www.semanticscholar.org/paper/Advances-in-Automated-Knowledge-Base-Construction-Suchanek/709e64be9cc9eb7c8b29bf49237cd2df835efd24)\\]\n* Information Extraction (IEEE Intelligent Systems, 2015) \\[[paper](https://ieeexplore.ieee.org/abstract/document/7243219)\\]\n* Populating Knowledge Bases (Part of The Information Retrieval Series book series, 2018) \\[[paper](https://link.springer.com/chapter/10.1007/978-3-319-93935-3_6)\\]\n* A Survey on Open Information Extraction (COLING 2018) \\[[paper](https://aclanthology.org/C18-1326.pdf)\\]\n* A Survey on Automatically Constructed Universal Knowledge Bases (Journal of Information Science, 2020) \\[[paper](https://journals.sagepub.com/doi/abs/10.1177/0165551520921342)\\]\n* Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases (Foundations and Trends in Databases, 2021) [[paper](https://dl.acm.org/doi/10.1561/1900000064)\\]\n* A Survey on Knowledge Graphs: Representation, Acquisition and Applications (TNNLS, 2021) \\[[paper](https://ieeexplore.ieee.org/document/9416312)\\]\n* Neural Symbolic Reasoning with Knowledge Graphs: Knowledge Extraction, Relational Reasoning, and Inconsistency Checking (Fundamental Research, 2021) \\[[paper](https://www.sciencedirect.com/science/article/pii/S266732582100159X)\\]\n* A Survey on Neural Open Information Extraction: Current Status and Future Directions (IJCAI 2022) \\[[paper](https://www.ijcai.org/proceedings/2022/0793.pdf)\\]\n* A Survey of Information Extraction Based on Deep Learning (Applied Sciences, 2022) \\[[paper](https://www.mdpi.com/2076-3417/12/19/9691)\\]\n* Generative Knowledge Graph Construction: A Review (EMNLP 2022) \\[[paper](https://aclanthology.org/2022.emnlp-main.1.pdf)\\]\n* Multi-Modal Knowledge Graph Construction and Application: A Survey (TKDE, 2022) \\[[paper](https://ieeexplore.ieee.org/abstract/document/9961954)\\]\n* A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications (Mathematics, 2023) \\[[paper](https://www.mdpi.com/2227-7390/11/8/1815)\\]\n* Construction of Knowledge Graphs: State and Challenges (Submitted to Semantic Web Journal, 2023) \\[[paper](https://www.semantic-web-journal.net/content/construction-knowledge-graphs-state-and-challenges)\\]\n\n**LLM-based IE**\n\n* Empirical Study of Zero-Shot NER with ChatGPT (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.493.pdf)\\]\n* Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.710.pdf)\\]\n* Evaluating ChatGPT’s Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2304.11633)\\] \n* Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.14450)\\]\n* Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2309.03433)\\]\n* LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2311.09366)\\]\n* LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.13168)\\]\n* Large Language Models for Generative Information Extraction: A Survey (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2312.17617)\\]\n* Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.96.pdf)\\]\n* LLMaAA: Making Large Language Models as Active Annotators (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.872.pdf)\\]\n* Large Language Models and Knowledge Graphs: Opportunities and Challenges (TGDK, 2023) \\[[paper](https://drops.dagstuhl.de/storage/08tgdk/tgdk-vol001/tgdk-vol001-issue001/TGDK.1.1.2/TGDK.1.1.2.pdf)\\]\n* Unifying Large Language Models and Knowledge Graphs: A Roadmap (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2306.08302)\\]\n* Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2311.05876)\\]\n* Large Knowledge Model: Perspectives and Challenges (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2312.02706)\\]\n* Knowledge Bases and Language Models: Complementing Forces (RuleML+RR, 2023) \\[[paper](https://link.springer.com/chapter/10.1007/978-3-031-45072-3_1)\\]\n* StructGPT: A General Framework for Large Language Model to Reason over Structured Data (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.574.pdf)\\]\n* Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.360.pdf)\\]\n\u003c!--* Knowledge Extraction from Survey Data Using Neural Networks (Procedia Computer Science, 2013) \\[[paper](https://www.sciencedirect.com/science/article/pii/S1877050913010995)\\]--\u003e\n\n### Low-Resource NLP Learning\n* A Survey of Zero-Shot Learning: Settings, Methods, and Applications (TIST, 2019) \\[[paper](https://dl.acm.org/doi/10.1145/3293318)\\]\n* A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios (NAACL 2021) \\[[paper](https://aclanthology.org/2021.naacl-main.201.pdf)\\]\n* A Survey on Low-Resource Neural Machine Translation (IJCAI 2021) \\[[paper](https://www.ijcai.org/proceedings/2021/0629.pdf)\\]\n* Generalizing from a Few Examples: A Survey on Few-shot Learning (ACM Computing Surveys, 2021) \\[[paper](https://dl.acm.org/doi/10.1145/3386252)\\]\n* Knowledge-aware Zero-Shot Learning: Survey and Perspective (IJCAI 2021) \\[[paper](https://www.ijcai.org/proceedings/2021/0597.pdf)\\]\n* Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs (IJCAI 2023) \\[[paper](https://www.ijcai.org/proceedings/2023/0737.pdf)\\]\n* Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey (Proceedings of the IEEE, 2023) \\[[paper](https://ieeexplore.ieee.org/document/10144560)\\]\n* A Survey on Machine Learning from Few Samples (Pattern Recognition, 2023) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0031320323001802)\\]\n* Multi-Hop Knowledge Graph Reasoning in Few-Shot Scenarios (TKDE, 2023) \\[[paper](https://ieeexplore.ieee.org/document/10216353)\\]\n* An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (TACL, 2023) \\[[paper](https://aclanthology.org/2023.tacl-1.12.pdf)\\]\n* Efficient Methods for Natural Language Processing: A Survey (TACL, 2023) \\[[paper](https://aclanthology.org/2023.tacl-1.48.pdf)\\]\n\n\n## 🍎 Traditional Methods 🍎\n\n## 1 Exploiting Higher-Resource Data\n\n### Weakly Supervised Augmentation\n* Distant Supervision for Relation Extraction without Labeled Data (ACL 2009) \\[[paper](https://aclanthology.org/P09-1113.pdf)\\]\n* Modeling Missing Data in Distant Supervision for Information Extraction (TACL, 2013) \\[[paper](https://aclanthology.org/Q13-1030.pdf)\\]\n* Neural Relation Extraction with Selective Attention over Instances (ACL 2016) \\[[paper](https://aclanthology.org/P16-1200v2.pdf)\\]\n* Automatically Labeled Data Generation for Large Scale Event Extraction (ACL 2017) \\[[paper](https://aclanthology.org/P17-1038.pdf)\\]\n* CoType: Joint Extraction of Typed Entities and Relations\nwith Knowledge Bases (WWW 2017) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3038912.3052708)\\]\n* Adversarial Training for Weakly Supervised Event Detection (NAACL 2019) \\[[paper](https://aclanthology.org/N19-1105.pdf)\\]\n* Local Additivity Based Data Augmentation for Semi-supervised NER (EMNLP 2020) \\[[paper](https://aclanthology.org/2020.emnlp-main.95/)\\]\n* BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision (KDD 2020) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3394486.3403149)\\]\n* Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction (EMNLP 2021) \\[[paper](https://aclanthology.org/2021.emnlp-main.216.pdf)\\]\n* Noisy-Labeled NER with Confidence Estimation (NAACL 2021) \\[[paper](https://aclanthology.org/2021.naacl-main.269.pdf)\\]\n* ANEA: Distant Supervision for Low-Resource Named Entity Recognition (ICLR 2021, Workshop of Practical Machine Learning For Developing Countries) \\[[paper](https://arxiv.org/pdf/2102.13129.pdf)\\]\n* Finding Influential Instances for Distantly Supervised Relation Extraction (COLING 2022) \\[[paper](https://aclanthology.org/2022.coling-1.233.pdf)\\]\n* Better Sampling of Negatives for Distantly Supervised Named Entity Recognition (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.300.pdf)\\]\n* Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.813.pdf)\\]\n\u003c!--* Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions (AAAI 2017) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/10953)\\]--\u003e\n\u003c!--* Reinforcement Learning for Relation Classification From Noisy Data (AAAI 2018) \\[[paper](https://dl.acm.org/doi/abs/10.5555/3504035.3504744)\\]--\u003e\n\u003c!--* Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning  (ACL 2018) \\[[paper](https://aclanthology.org/P18-1199.pdf)\\]--\u003e\n\u003c!--* Learning Named Entity Tagger using Domain-Specific Dictionary (EMNLP 2018) \\[[paper](https://aclanthology.org/D18-1230.pdf)\\]--\u003e\n\n### Multimodal Augmentation\n* Visual Attention Model for Name Tagging in Multimodal Social Media (ACL 2018) \\[[paper](https://aclanthology.org/P18-1185.pdf)\\]\n* Visual Relation Extraction via Multi-modal Translation Embedding Based Model (PAKDD 2018) \\[[paper](https://link.springer.com/chapter/10.1007/978-3-319-93034-3_43)\\]\n* Cross-media Structured Common Space for Multimedia Event Extraction (ACL 2020) \\[[paper](https://aclanthology.org/2020.acl-main.230.pdf)\\]\n* Image Enhanced Event Detection in News Articles (AAAI 2020) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6437)\\]\n* Joint Multimedia Event Extraction from Video and Article (EMNLP 2021, Findings) \\[[paper](https://aclanthology.org/2021.findings-emnlp.8.pdf)\\]\n* Multimodal Relation Extraction with Efficient Graph Alignment (MM 2021) \\[[paper](https://dl.acm.org/doi/10.1145/3474085.3476968)\\]\n* Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion (SIGIR 2022) \\[[paper](https://dl.acm.org/doi/pdf/10.1145/3477495.3531992)\\]\n* Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction (NAACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-naacl.121.pdf)\\]\n\n\n### Multi-Lingual Augmentation\n* Low-Resource Named Entity Recognition with Cross-lingual, Character-Level Neural Conditional Random Fields (IJCNLP 2017) \\[[paper](https://aclanthology.org/I17-2016.pdf)\\]\n* Neural Relation Extraction with Multi-lingual Attention (ACL 2017) \\[[paper](https://aclanthology.org/P17-1004.pdf)\\]\n* Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer (IJCAI 2018) \\[[paper](https://www.ijcai.org/Proceedings/2018/0566.pdf)\\]\n* Event Detection via Gated Multilingual Attention Mechanism (AAAI 2018) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/11919)\\]\n* Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning (COLING 2022) \\[[paper](https://aclanthology.org/2022.coling-1.382.pdf)\\]\n* Cross-lingual Transfer Learning for Relation Extraction Using Universal Dependencies (Computer Speech \u0026 Language, 2022) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0885230821000711)\\]\n* Language Model Priming for Cross-Lingual Event Extraction (AAAI 2022) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/21307)\\]\n* Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.317.pdf)\\]\n* PRAM: An End-to-end Prototype-based Representation Alignment Model for Zero-resource Cross-lingual Named Entity Recognition (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.201.pdf)\\]\n* Retrieving Relevant Context to Align Representations for Cross-lingual Event Detection (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.135.pdf)\\]\n* Hybrid Knowledge Transfer for Improved Cross-Lingual Event Detection via Hierarchical Sample Selection (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.296.pdf)\\]\n\n\n### Auxiliary Knowledge Enhancement\n\n#### (1) Textual Knowledge (Type-related Knowledge \u0026 Synthesized Data)\n* Zero-Shot Relation Extraction via Reading Comprehension (CoNLL 2017) \\[[paper](https://aclanthology.org/K17-1034.pdf)\\]\n* Zero-Shot Open Entity Typing as Type-Compatible Grounding (EMNLP 2018) \\[[paper](https://aclanthology.org/D18-1231.pdf)\\]\n* Description-Based Zero-shot Fine-Grained Entity Typing (NAACL 2019) \\[[paper](https://aclanthology.org/N19-1087.pdf)\\]\n* Improving Event Detection via Open-domain Trigger Knowledge (ACL 2020) \\[[paper](https://aclanthology.org/2020.acl-main.522.pdf)\\]\n* ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning (NAACL 2021) \\[[paper](https://aclanthology.org/2021.naacl-main.272.pdf)\\]\n* MapRE: An Effective Semantic Mapping Approach for Low-resource Relation Extraction (EMNLP 2021) \\[[paper](https://aclanthology.org/2021.emnlp-main.212.pdf)\\]\n* Distilling Discrimination and Generalization Knowledge for Event Detection via Delta-Representation Learning (ACL 2021) \\[[paper](https://aclanthology.org/P19-1429.pdf)\\]\n* MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER  (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.160.pdf)\\]\n* Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction (EMNLP 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-emnlp.332.pdf)\\]\n* Low-Resource NER by Data Augmentation With Prompting (IJCAI 2022) [[paper](https://www.ijcai.org/proceedings/2022/0590.pdf)\\]\n* ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.8.pdf)\\]\n* Entity-to-Text based Data Augmentation for Various Named Entity Recognition Tasks (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.578.pdf)\\]\n* Improving Low-resource Named Entity Recognition with Graph Propagated Data Augmentation (ACL 2023, Short) \\[[paper](https://aclanthology.org/2023.acl-short.11.pdf)\\]\n* GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.649.pdf)\\]\n* Generating Labeled Data for Relation Extraction: A Meta Learning Approach with Joint GPT-2 Training (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.727.pdf)\\]\n* RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.369.pdf)\\]\n* S2ynRE: Two-stage Self-training with Synthetic Data for Low-resource Relation Extraction  (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.455.pdf)\\]\n* Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.745.pdf)\\]\n* DAFS: A Domain Aware Few Shot Generative Model for Event Detection (Machine Learning, 2023) \\[[paper](https://link.springer.com/article/10.1007/s10994-022-06198-5)\\]\n* Enhancing Few-shot NER with Prompt Ordering based Data Augmentation (arXiv, 2023) [[paper](https://arxiv.org/abs/2305.11791)\\]\n* SegMix: A Simple Structure-Aware Data Augmentation Method (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2311.09505)\\]\n* Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.642.pdf)\\]\n* Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.334.pdf)\\]\n* STAR: Boosting Low-Resource Event Extraction by Structure-to-Text Data Generation with Large Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2305.15090)\\]\n* LLM-DA: Data Augmentation via Large Language Models for\nFew-Shot Named Entity Recognition (arXiv, 2024) [[paper](https://arxiv.org/abs/2402.14568)\\]\n\u003c!--The last four work are LLM-based DA--\u003e\n\n#### (2) Structured Knowledge (KG \u0026 Ontology \u0026 Logical Rules)\n* Leveraging FrameNet to Improve Automatic Event Detection (ACL 2016) \\[[paper](https://aclanthology.org/P16-1201.pdf)\\]\n* DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph (SIGIR 2021) \\[[paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463113)\\]\n* Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (EMNLP 2020) \\[[paper](https://aclanthology.org/2020.emnlp-main.50.pdf)\\]\n* Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification (COLING 2020) \\[[paper](https://aclanthology.org/2020.coling-main.265.pdf)\\]\n* NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (WWW 2020) \\[[paper](https://dl.acm.org/doi/10.1145/3366423.3380282)\\]\n* Knowledge-aware Named Entity Recognition with Alleviating Heterogeneity (AAAI 2021) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/17603)\\]\n* OntoED: Low-resource Event Detection with Ontology Embedding (ACL 2021) \\[[paper](https://aclanthology.org/2021.acl-long.220.pdf)\\]\n* Low-resource Extraction with Knowledge-aware Pairwise Prototype Learning (Knowledge-Based Systems, 2022) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0950705121008467)\\]\n\u003c!--* Neuralizing Regular Expressions for Slot Filling (EMNLP 2021) \\[[paper](https://aclanthology.org/2021.emnlp-main.747.pdf)\\]--\u003e\n\n\n## 2 Developing Stronger Data-Efficient Models\n\n### Meta Learning\n\n#### For Low-Resource NER\n* Few-shot Classification in Named Entity Recognition Task (SAC 2019) \\[[paper](https://dl.acm.org/doi/10.1145/3297280.3297378)\\]\n* Enhanced Meta-Learning for Cross-Lingual Named Entity Recognition with Minimal Resources (AAAI 2020) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/64667)\\]\n* MetaNER: Named Entity Recognition with Meta-Learning (WWW 2020) \\[[paper](https://dl.acm.org/doi/10.1145/3366423.3380127)\\]\n* Meta-Learning for Few-Shot Named Entity Recognition (MetaNLP, 2021) \\[[paper](https://aclanthology.org/2021.metanlp-1.6.pdf)\\]\n* Decomposed Meta-Learning for Few-Shot Named Entity Recognition (ACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-acl.124.pdf)\\]\n* Label Semantics for Few Shot Named Entity Recognition (ACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-acl.155.pdf)\\]\n* Few-Shot Named Entity Recognition via Meta-Learning (TKDE, 2022) \\[[paper](https://doi.org/10.1109/TKDE.2020.3038670)\\]\n* Prompt-Based Metric Learning for Few-Shot NER (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.451.pdf)\\]\n* Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.203.pdf)\\]\n* HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition (CIKM 2023) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3583780.3614908)\\] \n* Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2302.07739)\\]\n* Causal Interventions-based Few-Shot Named Entity Recognition (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.01914)\\]\n* MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2108.11635)\\]\n\n#### For Low-Resource RE\n* Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification (AAAI 2019) \\[[paper](https://ojs.aaai.org//index.php/AAAI/article/view/4604)\\]\n* Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs (ICML 2020) \\[[paper](http://proceedings.mlr.press/v119/qu20a/qu20a.pdf)\\]\n* Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction (COLING 2020) \\[[paper](https://aclanthology.org/2020.coling-main.563.pdf)\\]\n* Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (COLING 2020) \\[[paper](https://aclanthology.org/2020.coling-main.140.pdf)\\]\n* Prototypical Representation Learning for Relation Extraction (ICLR 2021)  \\[[paper](https://openreview.net/forum?id=aCgLmfhIy_f)\\]\n* Pre-training to Match for Unified Low-shot Relation Extraction (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.397.pdf)\\]\n* Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation Extraction (NAACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-naacl.139.pdf)\\]\n* fmLRE: A Low-Resource Relation Extraction Model Based on Feature Mapping Similarity Calculation (AAAI 2023) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/26605)\\]\n* Interaction Information Guided Prototype Representation Rectification for Few-Shot Relation Extraction (Electronics, 2023) \\[[paper](https://www.mdpi.com/2079-9292/12/13/2912)\\]\n* Consistent Prototype Learning for Few-Shot Continual Relation Extraction (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.409.pdf)\\]\n* RAPL: A Relation-Aware Prototype Learning Approach for\nFew-Shot Document-Level Relation Extraction (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.316.pdf)\\]\n* Density-Aware Prototypical Network for Few-Shot Relation Classification (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.162.pdf)\\]\n* Improving few-shot relation extraction through semantics-guided learning (Neural Networks, 2023) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0893608023006196)\\]\n* Generative Meta-Learning for Zero-Shot Relation Triplet Extraction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.01920)\\]\n\n#### For Low-Resource EE\n* Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection (WSDM 2020) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3336191.3371796)\\]\n* Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection (ACL 2021, Findings) \\[[paper](https://aclanthology.org/2021.findings-acl.214.pdf)\\]\n* Few-Shot Event Detection with Prototypical Amortized Conditional Random Field (ACL 2021, Findings) \\[[paper](https://aclanthology.org/2021.findings-acl.3.pdf)\\]\n* Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.440.pdf)\\]\n* MultiPLe: Multilingual Prompt Learning for Relieving Semantic Confusions in Few-shot Event Detection (CIKM 2023) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3583780.3614984)\\]\n\n\n### Transfer Learning\n* Zero-Shot Transfer Learning for Event Extraction (ACL 2018) \\[[paper](https://aclanthology.org/P18-1201.pdf)\\]\n* Transfer Learning for Named-Entity Recognition with Neural Networks (LREC 2018) \\[[paper](https://aclanthology.org/L18-1708.pdf)\\]\n* Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (NAACL 2019) \\[[paper](https://aclanthology.org/N19-1306.pdf)\\]\n* Relation Adversarial Network for Low Resource Knowledge Graph Completion (WWW 2020) \\[[paper](https://dl.acm.org/doi/pdf/10.1145/3366423.3380089)\\]\n* MZET: Memory Augmented Zero-Shot Fine-grained Named Entity Typing (COLING 2020) \\[[paper](https://aclanthology.org/2020.coling-main.7.pdf)\\]\n* LearningToAdapt with Word Embeddings: Domain Adaptation of Named Entity Recognition Systems (Information Processing and Management, 2021) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0306457321000455)\\]\n* One Model for All Domains: Collaborative Domain-Prefx Tuning for Cross-Domain NER (IJCAI 2023) \\[[paper](https://www.ijcai.org/proceedings/2023/0559.pdf)\\]\n* MANNER: A Variational Memory-Augmented Model for Cross Domain Few-Shot Named Entity Recognition (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.234.pdf)\\]\n* Linguistic Representations for Fewer-shot Relation Extraction across Domains (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.414.pdf)\\]\n* Few-Shot Relation Extraction With Dual Graph Neural Network Interaction (TNNLS, 2023) \\[[paper](https://ieeexplore.ieee.org/document/10143375)\\]\n* Leveraging Open Information Extraction for Improving Few-Shot Trigger Detection Domain Transfer (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.14163)\\]\n\n\n### Fine-Tuning PLM\n* Matching the Blanks: Distributional Similarity for Relation Learning (ACL 2019) \\[[paper](https://aclanthology.org/P19-1279.pdf)\\]\n* Exploring Pre-trained Language Models for Event Extraction and Generation (ACL 2019) \\[[paper](https://aclanthology.org/P19-1522.pdf)\\]\n* Coarse-to-Fine Pre-training for Named Entity Recognition (EMNLP 2020) \\[[paper](https://aclanthology.org/2020.emnlp-main.514.pdf)\\]\n* CLEVE: Contrastive Pre-training for Event Extraction (ACL 2021) \\[[paper](https://aclanthology.org/2021.acl-long.491.pdf)\\]\n* Unleash GPT-2 Power for Event Detection (ACL 2021) \\[[paper](https://aclanthology.org/2021.acl-long.490.pdf)\\]\n* Efficient Zero-shot Event Extraction with Context-Definition Alignment (EMNLP 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-emnlp.531.pdf)\\]\n* Few-shot Named Entity Recognition with Self-describing Networks (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.392.pdf)\\]\n* Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding (ACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-acl.16.pdf)\\]\n* ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.183.pdf)\\]\n* Unleashing Pre-trained Masked Language Model Knowledge for Label Signal Guided Event Detection (DASFAA 2023) \\[[paper](https://link.springer.com/chapter/10.1007/978-3-031-30675-4_42)\\]\n* A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER (CIKM 2023) \\[[paper](https://dl.acm.org/doi/10.1145/3583780.3614766)\\]\n* Continual Contrastive Finetuning Improves Low-Resource Relation Extraction (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.739.pdf)\\]\n* Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.433.pdf)\\]\n* GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2311.08526)\\]\n* Synergistic Anchored Contrastive Pre-training for Few-Shot Relation Extraction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2312.12021)\\]\n\n\n\n## 3 Optimizing Data and Models Together\n\n### Multi-Task Learning\n\n#### (1) IE \u0026 IE-Related Tasks\n\n**NER, Named Entity Normalization (NEN)**\n\n* A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization (AAAI 2019) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/3861)\\]\n* MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization (AAAI 2021) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/17714)\\]\n* An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (ACL 2021) \\[[paper](https://aclanthology.org/2021.acl-long.485.pdf)\\]\n\n**Word Sense Disambiguation (WSD), Event Detection (ED)**\n\n* Similar but not the Same: Word Sense Disambiguation Improves Event Detection via Neural Representation Matching (EMNLP 2018) \\[[paper](https://aclanthology.org/D18-1517.pdf)\\]\n* Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection (SIGIR 2021) \\[[paper](https://dl.acm.org/doi/pdf/10.1145/3404835.3463054)\\]\n\n#### (2) Joint IE \u0026 Other Structured Prediction Tasks\n\n**NER, RE**\n\n* GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (ACL 2019) \\[[paper](https://aclanthology.org/P19-1136.pdf)\\]\n* CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning (AAAI 2020) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/6495)\\]\n* Joint Entity and Relation Extraction Model based on Rich Semantics (Neurocomputing, 2021) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0925231220319378?casa_token=jzgLW9J1UKoAAAAA:5vnqqGKt0_-ykbhTp15Bq8mB-8B50cM3LDa10q2h8yc4q4AJVfeEbQV_fyMo2Z92xjl3HPNt6w)\\]\n\n**NER, RE, EE**\n\n* Entity, Relation, and Event Extraction with Contextualized Span Representations (EMNLP 2019) \\[[paper](https://aclanthology.org/D19-1585.pdf)\\]\n\n**NER, RE, EE  \u0026 Other Structured Prediction Tasks**\n\n* SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.21.pdf)\\]\n* Mirror: A Universal Framework for Various Information Extraction Tasks (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.548.pdf)\\]\n\n\n### Task Reformulation\n* Zero-Shot Relation Extraction via Reading Comprehension (CoNLL 2017) \\[[paper](https://aclanthology.org/K17-1034.pdf)\\]\n* Entity-Relation Extraction as Multi-Turn Question Answering (ACL 2019) \\[[paper](http://aclanthology.lst.uni-saarland.de/P19-1129.pdf)\\]\n* A Unified MRC Framework for Named Entity Recognition (ACL 2020) \\[[paper](https://aclanthology.org/2020.acl-main.519.pdf)\\]\n* Event Extraction as Machine Reading Comprehension (EMNLP 2020) \\[[paper](https://aclanthology.org/2020.emnlp-main.128.pdf)\\]\n* Event Extraction by Answering (Almost) Natural Questions (EMNLP 2020) \\[[paper](https://aclanthology.org/2020.emnlp-main.49.pdf)\\]\n* Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (ACL 2021) \\[[paper](https://aclanthology.org/2021.acl-long.217.pdf)\\]\n* Structured Prediction as Translation between Augmented Natural Languages (ICLR 2021) \\[[paper](https://openreview.net/forum?id=US-TP-xnXI)\\]\n* A Unified Generative Framework for Various NER Subtasks (ACL 2021) \\[[paper](https://aclanthology.org/2021.acl-long.451.pdf)\\]\n* REBEL: Relation Extraction By End-to-end Language Generation (EMNLP 2021, Findings) \\[[paper](https://aclanthology.org/2021.findings-emnlp.204.pdf)\\]\n* GenIE: Generative Information Extraction (NAACL 2022) \\[[paper](https://aclanthology.org/2022.naacl-main.342.pdf)\\]\n* Learning to Ask for Data-Efficient Event Argument Extraction (AAAI 2022, Student Abstract) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/21686)\\]\n* Complex Question Enhanced Transfer Learning for Zero-shot Joint Information Extraction (TASLP, 2023) \\[[paper](https://ieeexplore.ieee.org/abstract/document/10214665)\\]\n* Weakly-Supervised Questions for Zero-Shot Relation Extraction (EACL 2023) \\[[paper](https://aclanthology.org/2023.eacl-main.224.pdf)\\]\n* Event Extraction as Question Generation and Answering (ACL 2023, Short) \\[[paper](https://aclanthology.org/2023.acl-short.143.pdf)\\]\n* Set Learning for Generative Information Extraction (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.806.pdf)\\]\n\n### Prompt-Tuning PLM\n#### (1) Vanilla Prompt-Tuning\n* Template-Based Named Entity Recognition Using BART (ACL 2021, Findings) \\[[paper](https://aclanthology.org/2021.findings-acl.161.pdf)\\]\n* Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction (EMNLP 2021) \\[[paper](https://aclanthology.org/2021.emnlp-main.92.pdf)\\]\n* LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (COLING 2022) \\[[paper](https://aclanthology.org/2022.coling-1.209.pdf)\\]\n* COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition (COLING 2022) \\[[paper](https://aclanthology.org/2022.coling-1.222.pdf)\\]\n* Template-free Prompt Tuning for Few-shot NER (NAACL 2022) \\[[paper](https://aclanthology.org/2022.naacl-main.420.pdf)\\]\n* Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning (NAACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-naacl.187.pdf)\\]\n* RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction (ACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-acl.5.pdf)\\]\n* Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.466.pdf)\\]\n* Dynamic Prefix-Tuning for Generative Template-based Event Extraction (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.358.pdf)\\]\n* Good Examples Make A Faster Learner Simple Demonstration-based Learning for Low-resource NER (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.192.pdf)\\]\n* Prompt-Learning for Cross-Lingual Relation Extraction (IJCNN 2023) \\[[paper](https://arxiv.org/abs/2304.10354)\\]\n* DSP: Discriminative Soft Prompts for Zero-Shot Entity and Relation Extraction (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.339.pdf)\\]\n* Contextualized Soft Prompts for Extraction of Event Arguments (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.266.pdf)\\]\n* The Art of Prompting: Event Detection based on Type Specific Prompts (ACL 2023, Short) \\[[paper](https://aclanthology.org/2023.acl-short.111.pdf)\\]\n* Prompt for Extraction: Multiple Templates Choice Model for Event Extraction (KBS, 2024) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0950705124001795)\\]\n* UMIE: Unified Multimodal Information Extraction with Instruction Tuning (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2401.03082)\\]\n\n#### (2) Augmented Prompt-Tuning\n* PTR: Prompt Tuning with Rules for Text Classification (AI Open, 2022) \\[[paper](https://www.sciencedirect.com/science/article/pii/S2666651022000183)\\]\n* KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction (WWW 2022) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3485447.3511998)\\]\n* Ontology-enhanced Prompt-tuning for Few-shot Learning (WWW 2022) \\[[paper](https://dl.acm.org/doi/10.1145/3485447.3511921)\\]\n* Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning (SIGIR 2022, Short) \\[[paper](https://dl.acm.org/doi/abs/10.1145/3477495.3531746)\\]\n* Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning (NeurIPS 2022) \\[[paper](https://proceedings.neurips.cc/paper_files/paper/2022/file/97011c648eda678424f9292dadeae72e-Paper-Conference.pdf)\\]\n* AugPrompt: Knowledgeable Augmented-Trigger Prompt for Few-Shot Event Classification (Information Processing \u0026 Management, 2022) \\[[paper](https://www.sciencedirect.com/science/article/abs/pii/S0306457322002540)\\]\n* Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (NAACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-naacl.196.pdf)\\]\n* DEGREE: A Data-Efficient Generation-Based Event Extraction Model (NAACL 2022) \\[[paper](https://aclanthology.org/2022.naacl-main.138.pdf)\\]\n* Retrieval-Augmented Generative Question Answering for Event Argument Extraction (EMNLP 2022) \\[[paper](https://aclanthology.org/2022.emnlp-main.307.pdf)\\]\n* Unified Structure Generation for Universal Information Extraction (ACL 2022) \\[[paper](https://aclanthology.org/2022.acl-long.395.pdf)\\]\n* LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model (NeurIPS 2022) \\[[paper](https://proceedings.neurips.cc/paper_files/paper/2022/hash/63943ee9fe347f3d95892cf87d9a42e6-Abstract-Conference.html)\\]\n* Universal Information Extraction as Unified Semantic Matching (AAAI 2023) \\[[paper](https://ojs.aaai.org/index.php/AAAI/article/view/26563)\\]\n* Universal Information Extraction with Meta-Pretrained Self-Retrieval (ACL 2023) \\[[paper](https://aclanthology.org/2023.findings-acl.251.pdf)\\]\n* RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.1024.pdf)\\]\n* Schema-aware Reference as Prompt Improves Data-Efficient Relational Triple and Event Extraction (SIGIR 2023) \\[[paper](https://arxiv.org/abs/2210.10709)\\]\n* PromptNER: Prompt Locating and Typing for Named Entity Recognition (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.698.pdf)\\]\n* Focusing, Bridging and Prompting for Few-shot Nested Named Entity Recognition (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.164.pdf)\\]\n* Revisiting Relation Extraction in the era of Large Language Models (ACL 2023) [[paper](https://aclanthology.org/2023.acl-long.868.pdf)\\]\n* AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.615.pdf)\\]\n* BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.309.pdf)\\]\n* Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.17.pdf)\\]\n* Easy-to-Hard Learning for Information Extraction (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.754.pdf)\\]\n* DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.121.pdf)\\]\n* 2INER: Instructive and In-Context Learning on Few-Shot Named Entity Recognition (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.259.pdf)\\]\n* Template-Free Prompting for Few-Shot Named Entity Recognition via Semantic-Enhanced Contrastive Learning (TNNLS, 2023) \\[[paper](https://ieeexplore.ieee.org/abstract/document/10264144)\\]\n* TaxonPrompt: Taxonomy-Aware Curriculum Prompt Learning for Few-Shot Event Classification (KBS, 2023) \\[[paper](https://www.sciencedirect.com/science/article/pii/S0950705123000400)\\]\n* A Composable Generative Framework based on Prompt Learning for Various Information Extraction Tasks (IEEE Transactions on Big Data, 2023) \\[[paper](https://ieeexplore.ieee.org/abstract/document/10130644)\\]\n* Event Extraction With Dynamic Prefix Tuning and Relevance Retrieval (TKDE, 2023) \\[[paper](https://doi.org/10.1109/TKDE.2023.3266495)\\]\n* MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection (Information Processing \u0026 Management, 2023) \\[[paper](https://www.sciencedirect.com/science/article/abs/pii/S0306457323002467)\\]\n* PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.12217)\\]\n* TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2306.03974)\\]\n* OntoType: Ontology-Guided Zero-Shot Fine-Grained Entity Typing with Weak Supervision from Pre-Trained Language Models  (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.12307)\\]\n\n\n## 🍏 LLM-Based Methods 🍏\n\n## Direct Inference Without Tuning\n\n### Instruction Prompting\n* Exploring the Feasibility of ChatGPT for Event Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2303.03836)\\]\n* Zero-Shot Information Extraction via Chatting with ChatGPT (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2302.10205)\\]\n* Global Constraints with Prompting for Zero-Shot Event Argument Classification (EACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-eacl.191.pdf)\\]\n* Revisiting Large Language Models as Zero-shot Relation Extractors (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.459.pdf)\\]\n* Empirical Study of Zero-Shot NER with ChatGPT (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.493.pdf)\\]\n* AutoKG: Efficient Automated Knowledge Graph Generation for Language Models (IEEE BigData 2023, GTA3 Workshop) \\[[paper](https://arxiv.org/abs/2311.14740)\\]\n* PromptNER : Prompting For Named Entity Recognition (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.15444)\\]\n* Zero-shot Temporal Relation Extraction with ChatGPT (ACL 2023, BioNLP) \\[[paper](https://aclanthology.org/2023.bionlp-1.7.pdf)\\]\n* Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2304.11633)\\] \n* LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.13168)\\]\n* Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.11142)\\]\n* A Simple but Effective Approach to Improve Structured Language Model\nOutput for Information Extraction (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.13364)\\]\n\n### Code Prompting\n* Code4Struct: Code Generation for Few-Shot Event Structure Prediction (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.202.pdf)\\]\n* CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.855.pdf)\\]\n* ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.824.pdf)\\]\n* Retrieval-Augmented Code Generation for Universal Information Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.02962)\\]\n* CodeKGC: Code Language Model for Generative Knowledge Graph Construction (arXiv, 2023) [[paper](https://arxiv.org/abs/2304.09048)\\]\n* GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2310.03668)\\]\n\n### In-Context Learning\n* Learning In-context Learning for Named Entity Recognition (ACL 2023) \\[[paper](https://aclanthology.org/2023.acl-long.764.pdf)\\]\n* How to Unleash the Power of Large Language Models for Few-shot Relation Extraction? (ACL 2023, SustaiNLP Workshop) [[paper](https://aclanthology.org/2023.sustainlp-1.13.pdf)\\]\n* GPT-RE: In-context Learning for Relation Extraction using Large Language Models (EMNLP 2023) [[paper](https://aclanthology.org/2023.emnlp-main.214.pdf)\\]\n* In-context Learning for Few-shot Multimodal Named Entity Recognition (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.196.pdf)\\]\n* Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.184.pdf)\\]\n* Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples! (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.710.pdf)\\]\n* Guideline Learning for In-Context Information Extraction (EMNLP 2023) [[paper](https://aclanthology.org/2023.emnlp-main.950.pdf)\\]\n* Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction (EMNLP 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-emnlp.153.pdf)\\]\n* Pipeline Chain-of-Thought: A Prompt Method for Large Language Model Relation Extraction (IALP 2023) [[paper](https://ieeexplore.ieee.org/document/10337264)\\]\n* GPT-NER: Named Entity Recognition via Large Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2304.10428)\\]\n* In-Context Few-Shot Relation Extraction via Pre-Trained Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2310.11085)\\]\n* Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.08921)\\]\n* Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2305.14450)\\]\n* GPT Struct Me: Probing GPT Models on Narrative Entity Extraction (arXiv, 2023) [[paper](https://arxiv.org/abs/2311.14583)\\]\n* Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2309.03433)\\]\n* LOKE: Linked Open Knowledge Extraction for Automated Knowledge Graph Construction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2311.09366)\\]\n* Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2312.01954)\\]\n* Chain-of-Thought Prompt Distillation for Multimodal Named Entity Recognition and Multimodal Relation Extraction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2306.14122v3)\\]\n* Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction (COLING 2024) \\[[paper](https://arxiv.org/abs/2402.13741)\\]\n* LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.10573)\\]\n* GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.10744)\\]\n* C-ICL: Contrastive In-context Learning for Information Extraction (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.11254)\\]\n* EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.11430)\\]\n* Small Language Model Is a Good Guide for Large Language Model in Chinese Entity Relation Extraction (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.14373)\\]\n\n\u003c!--### Retrieval-Augmented Prompting--\u003e\n\n\n\n## Model Specialization With Tuning\n\n### Prompt-Tuning LLM\n* DeepStruct: Pretraining of Language Models for Structure Prediction (ACL 2022, Findings) \\[[paper](https://aclanthology.org/2022.findings-acl.67v2.pdf)\\]\n* Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (ACL 2023, Findings) \\[[paper](https://aclanthology.org/2023.findings-acl.50.pdf)\\]\n* Instruct and Extract: Instruction Tuning for On-Demand Information Extraction (EMNLP 2023) \\[[paper](https://aclanthology.org/2023.emnlp-main.620.pdf)\\]\n* UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition (ICLR 2024) [[paper](https://openreview.net/forum?id=r65xfUb76p)\\]\n* InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2304.08085)\\]\n* YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction (arXiv, 2023) \\[[paper](https://arxiv.org/abs/2312.15548)\\]\n* ChatUIE: Exploring Chat-based Unified Information Extraction using Large Language Models (COLING 2024) \\[[paper](https://arxiv.org/abs/2403.05132)\\]\n\n### Fine-Tuning LLM\n* Fine-Tuning GPT Family (OpenAI, 2023) \\[[Documentation](https://platform.openai.com/docs/guides/fine-tuning)\\]\n* EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models (arXiv, 2024) \\[[paper](https://arxiv.org/abs/2402.11430)\\]\n\n\n\n## How to Cite\n\n📋 Thank you very much for your interest in our survey work. If you use or extend our survey, please cite the following paper:\n\n```bibtex\n@misc{2023_LowResIE,\n    author    = {Shumin Deng and\n                 Yubo Ma and\n                 Ningyu Zhang and\n                 Yixin Cao and\n                 Bryan Hooi},\n    title     = {Information Extraction in Low-Resource Scenarios: Survey and Perspective}, \n    journal   = {CoRR},\n    volume    = {abs/2202.08063},\n    year      = {2023},\n    url       = {https://arxiv.org/abs/2202.08063}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzjunlp%2Flow-resource-kepapers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzjunlp%2Flow-resource-kepapers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzjunlp%2Flow-resource-kepapers/lists"}