Built on

E. Agichtein and L. Gravano, Snowball: Extracting Relations from Large Plain-Text Collections, in: Proceedings of the Fifth ACM Conference on Digital Libraries
2000
Earlier work this paper cites.
I. Hendrickx, S.N. Kim, Z. Kozareva, P. Nakov, D. Ó Séaghdha, S. Padó, M. Pennacchiotti, L. Romano and S. Szpakowicz, SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals, in: Proceedings of the 5th International Workshop on Semantic Evaluation
2010
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R. Grishman, Information Extraction, IEEE Expert
2015
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Y. Zhang, V. Zhong, D. Chen, G. Angeli and C.D. Manning, Position-aware Attention and Supervised Data Improve Slot Filling, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
2017
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S. Pawar, G.K. Palshikar and P. Bhattacharyya, Relation Extraction : A Survey, arXiv, 2017
2017
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N. Reimers and I. Gurevych, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
2019
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A.D. Cohen, S. Rosenman and Y. Goldberg, Relation classification as two-way span-prediction, arXiv preprint arXiv:2010.04829
Original
2020
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel and D. Kiela, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, in: Proceedings of the 34th International Conference on Neural Information Processing Systems
2020
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C. Alt, A. Gabryszak and L. Hennig, TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
2020
Earlier work this paper cites.

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M. Aydar, O. Bozal and F. Ozbay, Neural relation extraction: a survey, arXiv, 2020
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Then

H. Touvron, L. Martin, K.R. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D.M. Bikel, L. Blecher, C.C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A.S. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I.M. Kloumann, A.V. Korenev, P.S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E.M. Smith, R. Subramanian, X. Tan, B. Tang, R. Taylor, A. Williams, J.X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov and T. Scialom, Llama 2: Open Foundation and Fine-Tuned Chat Models, ArXiv
Original
2023
Later among the works it cites.
A.Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D.S. Chaplot, D.d.l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier et al., Mistral 7B, arXiv preprint arXiv:2310.06825
Original
2023
Later among the works it cites.
Y.S. Kai Zhang Bernal Jiménez Gutiérrez, Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors, in: Findings of ACL
2023
Later among the works it cites.

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2024

Retrieval-Augmented Generation-based Relation Extraction

Efeoglu, Sefika, Paschke, Adrian

Understand

Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies.

The identification of the relation between a pair of entities plays a crucial role within this framework.
Despite the existence of various techniques for relation extraction, their efficacy heavily relies on access to labeled data and substantial computational resources.
In addressing these challenges, Large Language Models (LLMs) emerge as promising solutions; however, they might return hallucinating responses due to their own training data.

Built on

E. Agichtein and L. Gravano, Snowball: Extracting Relations from Large Plain-Text Collections, in: Proceedings of the Fifth ACM Conference on Digital Libraries
2000
Earlier work this paper cites.
I. Hendrickx, S.N. Kim, Z. Kozareva, P. Nakov, D. Ó Séaghdha, S. Padó, M. Pennacchiotti, L. Romano and S. Szpakowicz, SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals, in: Proceedings of the 5th International Workshop on Semantic Evaluation
2010
Earlier work this paper cites.
R. Grishman, Information Extraction, IEEE Expert
2015
Earlier work this paper cites.
Y. Zhang, V. Zhong, D. Chen, G. Angeli and C.D. Manning, Position-aware Attention and Supervised Data Improve Slot Filling, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
2017
Earlier work this paper cites.
S. Pawar, G.K. Palshikar and P. Bhattacharyya, Relation Extraction : A Survey, arXiv, 2017
2017
Earlier work this paper cites.
N. Reimers and I. Gurevych, Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
2019
Earlier work this paper cites.
A.D. Cohen, S. Rosenman and Y. Goldberg, Relation classification as two-way span-prediction, arXiv preprint arXiv:2010.04829
Original
2020
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel and D. Kiela, Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, in: Proceedings of the 34th International Conference on Neural Information Processing Systems
2020
Earlier work this paper cites.
C. Alt, A. Gabryszak and L. Hennig, TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task, in: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
2020
Earlier work this paper cites.

Similar

M. Aydar, O. Bozal and F. Ozbay, Neural relation extraction: a survey, arXiv, 2020
2020
Cited alongside, same era.
G. Stoica, E.A. Platanios and B. Poczos, Re-TACRED: Addressing Shortcomings of the TACRED Dataset, Proceedings of the AAAI Conference on Artificial Intelligence
2021
Cited alongside, same era.
C. Wang, X. Liu, Z. Chen, H. Hong, J. Tang and D. Song, DeepStruct: Pretraining of Language Models for Structure Prediction, in: Findings of the Association for Computational Linguistics: ACL 2022
2022
Cited alongside, same era.
W. Zhou and M. Chen, An Improved Baseline for Sentence-level Relation Extraction, in: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
2022
Cited alongside, same era.

Then

H. Touvron, L. Martin, K.R. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, D.M. Bikel, L. Blecher, C.C. Ferrer, M. Chen, G. Cucurull, D. Esiobu, J. Fernandes, J. Fu, W. Fu, B. Fuller, C. Gao, V. Goswami, N. Goyal, A.S. Hartshorn, S. Hosseini, R. Hou, H. Inan, M. Kardas, V. Kerkez, M. Khabsa, I.M. Kloumann, A.V. Korenev, P.S. Koura, M.-A. Lachaux, T. Lavril, J. Lee, D. Liskovich, Y. Lu, Y. Mao, X. Martinet, T. Mihaylov, P. Mishra, I. Molybog, Y. Nie, A. Poulton, J. Reizenstein, R. Rungta, K. Saladi, A. Schelten, R. Silva, E.M. Smith, R. Subramanian, X. Tan, B. Tang, R. Taylor, A. Williams, J.X. Kuan, P. Xu, Z. Yan, I. Zarov, Y. Zhang, A. Fan, M. Kambadur, S. Narang, A. Rodriguez, R. Stojnic, S. Edunov and T. Scialom, Llama 2: Open Foundation and Fine-Tuned Chat Models, ArXiv
Original
2023
Later among the works it cites.
A.Q. Jiang, A. Sablayrolles, A. Mensch, C. Bamford, D.S. Chaplot, D.d.l. Casas, F. Bressand, G. Lengyel, G. Lample, L. Saulnier et al., Mistral 7B, arXiv preprint arXiv:2310.06825
Original
2023
Later among the works it cites.
Y.S. Kai Zhang Bernal Jiménez Gutiérrez, Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors, in: Findings of ACL
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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B. Li, W. Ye, J. Zhang and S. Zhang, Reviewing Labels: Label Graph Network with Top-k Prediction Set for Relation Extraction, 2022

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H.W. Chung, L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, E. Li, X. Wang, M. Dehghani, S. Brahma, A. Webson, S.S. Gu, Z. Dai, M. Suzgun, X. Chen, A. Chowdhery, S. Narang, G. Mishra, A. Yu, V. Zhao, Y. Huang, A. Dai, H. Yu, S. Petrov, E.H. Chi, J. Dean, J. Devlin, A. Roberts, D. Zhou, Q.V. Le and J. Wei, Scaling Instruction-Finetuned Language Models, arXiv, 2022. doi:10.48550/ARXIV.2210.11416. https://arxiv.org/abs/2210.11416

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Later among the works it cites.

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Later among the works it cites.

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Original

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Later among the works it cites.

W. Xiong, Y. Song, P. Wang and S. Li, Rationale-Enhanced Language Models are Better Continual Relation Learners, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Later among the works it cites.

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