Cited by 213Topic ModelingNatural Language Processing TechniquesMultimodal Machine Learning Applications

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Revisiting Relation Extraction in the era of Large Language Models

Open access

Wadhwa, Somin, Amir, Silvio, Wallace, Byron·2023·Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

DOI 10.18653/v1/2023.acl-long.868PMC PMC10482322

Open this paper in CoChat — free account Open PDF

Abstract

Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text.Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them.Recent work has instead treated the problem as a sequence-tosequence task, linearizing relations between entities as target strings to be generated conditioned on the input.Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision.We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching.Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the fewshot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results.We release this model as a new baseline for RE tasks 1 .

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.18653/v1/2023.acl-long.868

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263
Support vector machine learning for interdependent and structured output spaces
Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015341 · Cited by 1,493
Scaling Instruction-Finetuned Language Models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441
Modeling Relations and Their Mentions without Labeled Text
Riedel, S., Yao, L., & McCallum, A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15939-8_10 · Cited by 1,333
Finetuned Language Models Are Zero-Shot Learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
Lu, Y., Bartolo, M., Moore, A., Riedel, S., & Stenetorp, P. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.556 · Cited by 610

Show all 32 references

Research works printed but not linked: 2

Brown Tom, Mann Benjamin, Ryder Nick, Subbiah Melanie, Jared D Kaplan Prafulla Dhariwal, Neelakantan Arvind, Shyam Pranav, Sastry Girish, Askell Amanda, Agarwal Sandhini, Herbert-Voss Ariel, Krueger Gretchen, Henighan Tom, Child Rewon, Ramesh Aditya, Ziegler Daniel, Wu Jeffrey, Winter Clemens, Hesse Chris, Chen Mark, Sigler Eric, Litwin Mateusz, Gray Scott, Chess Benjamin, Clark Jack, Berner Christopher, Sam McCandlish Alec Radford, Sutskever Ilya, and Amodei Dario. 2020a. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877– 1901. Curran Associates, Inc.
Paolini Giovanni, Athiwaratkun Ben, Krone Jason, Ma Jie, Achille Alessandro, ANUBHAI RISHITA, Santos Cicero Nogueira dos, Xiang Bing, and Soatto Stefano. 2021. Structured prediction as translation between augmented natural languages. In International Conference on Learning Representations.

Sorted by citations

Items 101–200 of 213

EduDCM: A Novel Framework for Automatic Educational Dialogue Classification Dataset Construction via Distant Supervision and Large Language Models
Qi, C., Zheng, L., Wei, Y., Xu, H., Chen, P., & Gu, X. (2024) · Applied Sciences · doi:10.3390/app15010154 · Cited by 2
Kastor: Fine-Tuned Small Language Models for Shape-Based Active Relation Extraction
Ringwald, C., Gandon, F. L., Zucker, C. F., Michel, F., & Akl, H. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-94575-5_6 · Cited by 3
Verbalization diversification for relation extraction with long-tailed entity augmentation
Liang, Y., Jiang, T., Xin, Y., Yan, C., Wang, L., Wang, X., & He, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130771 · Cited by 2
The mosaic memory of large language models
Shilov, I., Meeus, M., & de Montjoye, Y.-A. (2026) · Nature Communications · doi:10.1038/s41467-026-68603-0 · Cited by 24
Searching for LINCS to Stress: Using Text Mining to Automate Reference Chemical Curation
Chambers, B. A., Basili, D., Word, L. J., Baker, N., Middleton, A., Judson, R., & Shah, I. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00335 · Cited by 2
Similarity-Based Prompt Construction for Large Language Model in Medical Tasks
Liu, G., Pan, M., Ma, Z., Gu, M., Yang, L., & Qin, J. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1717-0_6 · Cited by 1
Chinese named entity recognition in the furniture domain based on ERNIE and adversarial learning
Song, Y., Jia, Y., & Zhang, J. (2024) · International Journal of Web Information Systems · doi:10.1108/ijwis-08-2024-0239 · Cited by 1
PRAGyan - Connecting the Dots in Tweets
Ravi, R., Ginde, G., & Rokne, J. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78548-1_25 · Cited by 2
LLM & Bagging for 1-shot Joint IE
Fu, Y., Ding, Z., & Xu, X. (2024) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc63484.2024.00034 · Cited by 1
Information Extraction from Aircraft Maintenance Records Using Large Language Models
Jia, L., Zhou, Q., Peng, H., Lu, H., Hao, Y., & Li, Y. (2024) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-beijing63284.2024.10874745 · Cited by 1
Distantly Supervised relation extraction with multi-level contextual information integration
Han, D., Huang, H., Shi, S., Yuan, C., & Guo, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129858 · Cited by 1
A neural kernel framework for relation extraction
Wang, K., Chen, Y., Yang, W., Huang, R., & Qin, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128049 · Cited by 1
Towards Scientific Knowledge Graphs: Dependency Graph Analysis Using Graph Neural Networks for Extracting Scientific Relations
Wu, R., & Ardakani, S. P. (2025) · Electronics · doi:10.3390/electronics14112276 · Cited by 2
How Important is Domain-Specific Language Model Pretraining and Instruction Finetuning for Biomedical Relation Extraction?
Brokman, A., & Kavuluru, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97141-9_6 · Cited by 4
Instruction Tuning with Data Augmentation for Event Argument Extraction
Wang, C., Wang, X., & Chen, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0014-7_22 · Cited by 1
Data Overdose? Time for a Quadruple Shot: Knowledge Graph Construction Using Enhanced Triple Extraction
Elliott, T. J., Levitt, S. P., Nixon, K. J., & Bekker, M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-96262-2_15 · Cited by 1
Large Language Model-Driven Approach for Automated Competency Knowledge Graph Construction in IT Domain
Dinh, D., Duong, H., & Nguyen, T. T. M. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-10209-6_13 · Cited by 1
Are Triggers Needed for Document-Level Event Extraction?
Shaar, S., Chen, W., Chatterjee, M., Wang, B., Zhao, W., & Cardie, C. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.51 · Cited by 3
LECITE: LoRA-Enhanced and Consistency-Guided Iterative Knowledge Graph Construction
Xiao, D., & Quan, Q. I. A. N. (2026) · Future Internet · doi:10.3390/fi18010032 · Cited by 1
URRel : Joint Entity and Relation Extraction in Chinese Medical Texts via Unified Representation and Reinforcement Learning With Overlapping Relations
Xu, Z., Tong, J., Tong, Y., Xia, S., Zhou, Q., & Shen, Y. (2026) · Expert Systems · doi:10.1111/exsy.70214 · Cited by 1
A Survey on Knowledge Graph Construction from Multi-Source Heterogeneous Data
Bai, J., Zhang, H., & Zhao, H. (2025) · International Symposium on Artificial Intelligence and Big Data (AIBDF) · doi:10.1109/aibdf67964.2025.11440788 · Cited by 1
Retrieval augmented generation (RAG) with enhanced SBERT fine-tuning vector summarization in medical domain
Hariharan, S., Balapriya, S., Jose, P. S. H., Xu, Q., Lu, J., & Kor, A.-L. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132388 · Cited by 1
Enhancing LLM-Based Data Annotation with Error Decomposition
Xu, Z., Khatri, V., Dai, Y., Liu, X., Li, S., Zhang, X., & Yu, R. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785070 · Cited by 5
Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering
Nishida, N., Munne, R. F., Liu, S., Tokunaga, N., Yamagata, Y., Cheng, F., Kozaki, K., & Matsumoto, Y. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.615 · Cited by 1
The LOPE Method: Improving Consistent Property Extraction for Scientific Knowledge Graphs Using LLMs
Schaftner, S., & Gaedke, M. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774905.3795079 · Cited by 1
The Mask One At a Time Framework for Detecting the Relationship between Financial Entities
Ghosh, S., Umrao, S., Chen, C.-C., & Naskar, S. K. (2023) · Annual Meeting of the Forum for Information Retrieval Evaluation (FIRE) · doi:10.1145/3632754.3632756 · Cited by 1
Large Model Fine-Tuning Method Based on Pre-Cognitive Inductive Reasoning - PCIR
Zhang, H., Wang, L., Shao, H., Wu, M., & Ren, W. (2024) · International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) · doi:10.1109/ainit61980.2024.10581497 · Cited by 1
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models
Otto, W., Upadhyaya, S., & Dietze, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65794-8_21 · Cited by 1
Distilling Event Sequence Knowledge From Large Language Models
Wadhwa, S., Hassanzadeh, O., Bhattacharjya, D., Barker, K., & Ni, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77844-5_13 · Cited by 2
Self-citation
Large Language Models for Few-Shot Named Entity Recognition
Zhao, Y., Zhong, X., Cambria, E., & Rajapakse, J. C. (2025) · AI Computer Science and Robotics Technology · doi:10.5772/acrt.20250103 · Cited by 1
Large Language Models for Data Extraction in Slot-Filling Tasks
Bazan, M., Gniazdowski, T., Wolkiewicz, D., Sarna, J., & Marchwiany, M. E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-61857-4_1
ABCD Team at SOMD 2024: Software Mention Detection in Scholarly Publications with Large Language Models
Xuan, P. N., Minh, Q. T., & Văn, T. Đ. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65794-8_19
Enhancing Relation Extraction Through Augmented Data: Large Language Models Unleashed
Ali, M., Nisar, M. S., Saleem, M., Moussallem, D., & Ngomo, A.-C. N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70242-6_7
Incorporating Feature Interaction into Relation Contrastive Learning for Zero-shot Relation Extraction
Gao, Z., Duan, J., & Zhou, X. W. (2024) · International Conference on Computational Linguistics and Natural Language Processing (CLNLP) · doi:10.1109/clnlp64123.2024.00012
BrainKnow: Extracting, Linking, and Synthesizing Neuroscience KnowledgePreprint
Huangfu, C., Sun, K., Zeng, Y., Wang, D., Wang, Y., & Ruan, Z. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5029560
SFDG-RE: Self-Feedback Description Generation Based on LLMs for Enhanced Zero-Shot Relation Extraction
Cheng, L., Chunhong, Z., & Tang, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0814-0_25
Approaches for extracting daily dosage from free-text prescription signatures in heart failure with reduced ejection fraction: a comparative study
Haaker, T. S., Choi, J. S., Nanjo, C. J., Warner, P. B., Abu–Hanna, A., & Kawamoto, K. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae153
Generative Prompt Engineering
Marcondes, F. S., Gala, A. D. C. O. S., Magalhães, R., de Britto, F. P., Durães, D. A., & Nováis, P. (2025) · SpringerBriefs in computer science · doi:10.1007/978-3-031-76631-2_4
Relation Extraction from Unstructured Species Descriptions Using TaxonNERD and Llama 7B
Montero, F. R., Villanueva, E. R., & Mora-Cross, M. A. (2024) · IEEE International Conference on BioInspired Processing (BIP) · doi:10.1109/bip63158.2024.10885387
Mix-Lingual Relation Extraction: Dataset and a Training Approach
Kong, L.-X., Chu, Y., Ma, Z., Zhang, J., & Chen, J. (2025) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-4314-y · Cited by 1
Innovative Techniques for Compressing Large Language Models without Performance LossPreprint
Lyda, H., Trish, B., & Brody, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5185149
ERNIE-UIE: Advancing information extraction in Chinese medical knowledge graph
Li, B., Li, C., Sun, J., Zeng, X., Chen, X., & Zheng, J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0325082 · Cited by 2
Leveraging Domain-Specific Databases for Seq2Seq-Based Relation Extraction from Materials Science Texts
Asada, M., & Fukuda, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93429-2_19
Research on Entity Relationship Extraction in Wind Turbine Fault Domain Based on Improved CasRel Model
张, 友. (2025) · Artificial Intelligence and Robotics Research · doi:10.12677/airr.2025.143066
Claim Verification Leveraging In-context Learning and Retrieval Augmented Generation
Fenza, G., Furno, D., Gallo, M., Loia, V., & Trotta, P. P. (2025) · Lecture notes in social networks · doi:10.1007/978-3-031-85386-9_2
Eeec:Emotion-Experiencer-Event-Cause Multi-Step Chain Reasoning for Emotion-Cause Pair ExtractionPreprint
Gu, X., Meng, Z., Tavares, A., Gomes, T., & Xu, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5342269
An Automated Medical Rdf Knowledge Graph Construction From Text Using in-Context Learning
Racharak, T., Wang, T., & Jearanaiwongkul, W. (2024) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse63888.2024.11063495 · Cited by 1
MCE: One-Shot Method to Relation Extraction Based on LLMs
Zhao, W., Wei, X., Meng, Q., Yu, G. J., & Jiang, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0006-2_30
Leveraging Large Language Models for Document-Level Complex Relation Extraction Through Self-Verification and Tool-Assisted Relational Reasoning
Wang, Y., Chen, Y., Wang, Z., Wen, L., & Zhao, W. Q. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9812-7_32
Impact of Large-Model Temperature on Generative Relation Extraction Results
Sun, X., Zhang, P., Zhao, M., & He, S. (2025) · IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI) · doi:10.1109/prmvai65741.2025.11108614
Reduction of supervision for biomedical knowledge discovery
Theodoropoulos, C., Coman, A. C., Henderson, J., & Moens, M. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06187-0
Topic-Driven Hyper-relational Knowledge Graphs with Adaptive Reconstruction for Multi-hop Question Answering Using LLMs
Zhang, Y., Cheng, B. H., & Chen, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04549-2_11
Exploration of Genetic Entity Extraction From Spanish Literature Using Generative LLMs
Kodikara, M., & Verspoor, K. M. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3612412
Comparative Study on Automatic Extraction of Genealogical Data from Ancient Text by Using Large Language Models and Open AI
Xavier, V. C. C. A., Alam, M. M., & Logofătu, D. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-87639-4_14
A Visualization System for LLM-based Fault Entity Extraction of Power Equipment
You, L., Guo, X., Wang, D., Zhang, Z., Zhong, K., Chen, N., & Wang, H. (2025) · doi:10.1109/icbdse65491.2025.11220034
Adaptive Chain-of-Thought Framework for Dynamic Reasoning in Adverse Drug Reaction Extraction and Standardization
Ren, X., Li, S., & Jiang, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3629943
IEAgent: A Zero-Shot Information Extraction Agent with Knowledge RAG for Unstructured Data
Sun, J., Sun, Z., Ren, M., & Zhang, Y. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227258
Comprehensive Argument Mining for Chinese Argumentative Essays Using Large Language Models
Song, Y., Yu, B., Zheng, A., Wu, P., Liu, T., Liu, X., Zan, H., & Zhang, K. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_30
Relational arcs as narrative structure: Dynamics, distribution and diachronic change in fiction
Christou, D., & Tsoumakas, G. (2025) · Computational Humanities Research · doi:10.1017/chr.2025.10021
Ent-BAM: A Generative Framework for Biomedical Argument Mining Enriched With Entity Information
Das, N., Choudhary, Y. S., Saradhi, V. V., & Anand, A. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3616076
Comparing Large Language Models for Food-Microbiome Relation Extraction from Research Papers
Dawadi, R., Tay, J. T., Morales, A. M., Vu, T., Hoang, P. T. N., Yamamoto, M., Watanabe, N., Kuriya, Y., & Araki, M. (2025) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3761712.3761763
United States Air Force Research Laboratory Investments in AI for Military Training and Education
Myers, T., D’amour, J. A., King, J. M., & Borghetti, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13174-4_16
A BERT-Assisted LLM Framework for Knowledge Graph Construction
Jiang, Y., Huang, K., Zhang, X., Xu, W., Liang, Z., Yi, Y., Xu, P., Dai, T., & Bi, L. (2025) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads67057.2025.11323011
Structure dependent weighted loss function for triple extraction using BART model
Shrivastava, M., Shibata, K., & Wagatsuma, H. (2026) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01604-9
KG-Orchestra: An Open-Source Multi-Agent Framework for Evidence-Based Biomedical Knowledge Graphs Enrichment
Mohamed, A. H., Shalaby, K. S., Kaladharan, A., Sen, S., Ataş, H., & Kodamullil, A. T. (2026) · Knowledge-Based Systems · doi:10.64898/2026.02.18.706536
Confidence-Aware Dual-Graph Calibration for Noise-Robust Relation Extraction in Low-Resource ScenariosPreprint
Suo, J., Fang, J., Wang, H., & Xiao, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-8872718/v1
LiteKG: A Lightweight LLM-Assisted Framework for Domain-Specific Knowledge Graph Construction
Ho, Y.-M., Ku, W.-P., & Hou, P.-Y. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401070
Hierarchical Knowledge Extraction of Grain Crop Pests and Diseases Based on Large Models
Zhang, Y., Zhang, M., & Zhu, H. (2025) · International Symposium on Artificial Intelligence and Big Data (AIBDF) · doi:10.1109/aibdf67964.2025.11440687
LinkUIE: Entity Linking Adapter for Universal Information Extraction
Xiong, W., Zheng, Y., Meng, W., Liu, B., Wang, D., & Zheng, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-8414-7_26
Natural Language Processing for Analyzing Messages of Astrophysical Observations
Alkan, A. K. (2024) · theses.fr (ABES) · doi:10.70675/ab93cd1az4468z404czb2b3z8dc680a04e4d
Unsupervised open-world information extraction from unstructured and domain-specific document collections
Genest, P.-Y. (2024) · theses.fr (ABES) · doi:10.70675/88c65002z4b8cz466dz923bz13954d6729bf
An Active Structure-Learning Strategy for LLMs-Based Document-Level Event Extraction
Zhang, J., Chen, Y., Zou, A., Huang, R., & Qin, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-7078-2_45
ERE-LLM: Entity-Relation Extraction with Large Language Model in Professional Domains
Liu, X. Y., Ding, Z., Gao, W., Li, Y., & Hu, K. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464200
Mitigating Entity Bias in Relation Extraction with Pair-Training
Jia, H., Zhu, T., Tan, C., & Chen, W. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464075
MAGE-KT: Multi-Agent Graph-Enhanced Knowledge Tracing with Subgraph Retrieval and Asymmetric Fusion
Yu, C., Yuan, H., & Duan, Z. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460480
E-GuidedRE: An Evaluation-Model-Guided Collaborative Framework for Relation Extraction in Specialized Domains
Liu, Y., Zhang, J. Q., Luan, R., & Yu, X. (2026) · Symmetry · doi:10.3390/sym18050761
Document-Level Relation Extraction Using Reinforcement Learning with Knowledge Graph Feedback
Ali, M., Zahera, H. M., Saleem, M., Mahmood, Y., Khan, H., Speck, R., & Ngomo, A.-C. N. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25156-5_28
Joint Inference for Informed End-to-End Knowledge Base Population
Kirsch, B., Beckh, K., Chackraborty, N., Heuser, S., & Stefan Rüping, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21480-5_15
Snippet-Driven Supply Chain Discovery with LLMs: Scaling Visibility in China
FUKADA, H., & Mizuno, T. (2026) · IEEE Conference on Computational Intelligence for Financial Engineering & Economics · doi:10.1109/cifer67845.2026.11692384
Enhancing dialogue relation extraction ability by incorporating the dialogue structure into the PLM-based encoder
Gao, J., Zhang, L., Wang, Q., & Mao, Z. (2026) · JUSTC · doi:10.52396/justc-2024-0009
A Consensus-Driven Information Extraction Method via Voting and Pruning over LLM Chatbot Outputs for a Novel Agricultural Dataset
Çelik, E. F., & Uğur, A. (2026) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/ichora69329.2026.11537216
Extensive Benchmark of Frugal Encoder–Decoder Language Models for Datatype Properties Extraction and RDF Knowledge Graph Generation
Ringwald, C., Gandon, F. L., Zucker, C. F., Michel, F., & Akl, H. A. (2026) · Semantic Web · doi:10.1177/22104968261452987
Syntax-Enhanced Contextual Representation Learning for Complex Relation Extraction via Attention-Guided Graph Network
Suo, J., Fang, J., Liu, J., Wang, M., & Xiao, Y. (2026) · Journal of Intelligent & Fuzzy Systems · doi:10.1177/18758967261457533
Enhancing LLMs for Manufacturing Information Extraction
Park, S., Lee, H., Lee, R., Suh, H.-W., & Song, K. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1468-6_21
DSE-RE: Dynamic Schema Evolution with LLM for Iterative Relation Extraction
Wang, T., Shi, D., Aguilar, J., & Zurada, J. M. (2026) · Human-Centric Intelligent Systems · doi:10.1007/s44230-026-00158-1
Line-level bug-finding power of static analysis rules: a case study of Teamscale
Ye, L., Nie, Y., Zhou, Y., Yang, Y., Lu, H., Qian, J., & Zhou, Y. (2026) · Empirical Software Engineering · doi:10.1007/s10664-026-10895-3
Structured Multi-extraction Prompting Improves LLM-Based Biomedical Relation Extraction Without Fine-Tuning
Labonté, F. M., Giri, S., Herbrik, C., & Flek, L. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-28819-6_39
Label Concreteness-Guided Diverse Data Synthesis for Low-Resource Relation Extraction
Liu, X., Hu, C., Zhang, R., Chen, J., & Xu, B. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3705560
Trustworthy LLM-Based Relational Triple Extraction via Entailment-Aware Collaboration
Huang, J. J., Yang, L. Y., & Xiao, B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2859-1_20
Difference-Aware Boundary Refinement for Named Entity Recognition
Ouyang, J., & Liao, W. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2852-2_11
Extract, Align, Reflect: An LLM-Based Framework for Knowledge Graph Construction
Huang, H., & Zhao, H. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3426-4_13
SE-AGGN: Syntax-Enhanced Relation Extraction via Attention-Guided Graphs and Gated Fusion
Suo, J., He, Z., & Ning, Y. (2026) · International Conference on Algorithms, Software Engineering and Network Security (ASENS) · doi:10.1109/asens69964.2026.11605279
GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks
Hadi, H. J., Khan, M. K., Ahmad, N., Yasmin, R., & Shoker, A. (2026) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2026.3716998 · Cited by 1
EvoMod: Evolutionary Optimization of Modular Prompts for Biomedical Relation Extraction
Yang, L., Zhan, J. Z., & Chen, J. (2026) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3807503.3819434
A Knowledge Graph Framework for Linking Health Assessment Scales and Scientific Literature in Low-Annotation Settings: Development and Evaluation Study
Sun, H., Zhao, L., Chen, Z., Li, J., Li, J., Hao, J., Lin, Y. D., & Qian, Q. (2026) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-026-00247-7
Mmf-re: psychological medicine entity relation extraction model based on multi-level feature enhancement
Liu, Z., Yang, X., Chang, Z., Ma, H., & Wang, D. (2026) · Health Information Science and Systems · doi:10.1007/s13755-026-00462-x
How Do Algorithms Interact in Scientific Research? Large-Scale Semantic Relation Identification and Analysis Based on Full-Text PapersPreprint
Wang, Y., Zhao, Y., Wu, W., & Zhang, H. (2026) · Research Square · doi:10.21203/rs.3.rs-10102565/v1
DyFIRER: A Dynamic Feedback Iterative Multi-Agent Framework for Open Relation Extraction Based on Large Language Models
Gong, Y., Shao, M., Lian, X., & Zhou, J. (2026) · Applied Sciences · doi:10.3390/app16178462
Reproducible Dual-Path NLP for Character Graph Extraction and Context-Aware State Tracking in Low-Resource Short Text
Liu, Y. (2026) · International Conference on Computer Information Science and Application Technology (CISAT) · doi:10.1109/cisat71030.2026.11682145
Knowledge Graphs for Railway Accident Profiling: Research and Applications
Lian, X., Wang, Z., Yuan, H., Gao, C., Cheng, Z., Wu, Y., & Zheng, G. (2026) · Applied Sciences · doi:10.3390/app16189021

Authors: Wadhwa, S., Amir, S., & Wallace, B.
Affiliations: Northeastern University (US)
ORCID: Wallace, Byron
Venue: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher: Association for Computational Linguistics
Published: 2023
Volume: 2023
Pages: 15566-15589
ISSN: 0736-587X (ISSN-L)
DOI: 10.18653/v1/2023.acl-long.868
PMC: PMC10482322
OpenAlex: W4385571451
Cited by: 207
References: 32
Citations by year: 2026: 53 · 2025: 74 · 2024: 65 · 2023: 14
FWCI: 36.50
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion, cc-by)
arXiv (Cornell University) (cc-by)
Funders: National Science Foundation (1750978)
National Institutes of Health (5r01lm012086-08)
U.S. National Library of Medicine (R01-LM012086)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords: relation extraction, large language models, GPT-3, Flan-T5, few-shot prompting, chain-of-thought prompting, human evaluation, sequence-to-sequence generation, entity relationships, fine-tuning

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Revisiting Relation Extraction in the era of Large Language Models

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks