Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models

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Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.t., Rocktäschel, T., Riedel, S., Kiela, D.: Retrieval-augmented generation for knowledge-intensive nlp tasks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 9459–9474. Curran Associates, Inc. (2020)

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Du, C.F., Cohoon, J., Lopez, P., Howison, J.: Softcite dataset: A dataset of software mentions in biomedical and economic research publications. Journal of the Association for Information Science and Technology 72

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Schindler, D., Bensmann, F., Dietze, S., Krüger, F.: Somesci- a 5 star open data gold standard knowledge graph of software mentions in scientific articles. Proceedings of the 30th ACM International Conference on Information & Knowledge Management pp. 4574–4583 (2021). https://doi.org/10.1145/3459637.3482017

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Saji, A., Matsubara, S.: Extracting information about research resources from scholarly papers. In: International Conference on Asian Digital Libraries. pp. 440–480 (2022). https://doi.org/10.1007/978-3-031-21756-2_35

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Cited alongside, same era.

Otto, W., Zloch, M., Gan, L., Karmakar, S., Dietze, S.: GSAP-NER: A novel task, corpus, and baseline for scholarly entity extraction focused on machine learning models and datasets. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023. pp. 8166–8176. Association for Computational Linguistics, Singapore (Dec 2023). https://doi.org/10.18653/v1/2023.findings-emnlp.548, https://aclanthology.org/2023.findings-emnlp.548

2023

Cited alongside, same era.

Wadhwa, S., Amir, S., Wallace, B.: Revisiting relation extraction in the era of large language models. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 15566–15589. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https://doi.org/10.18653/v1/2023.acl-long.868, https://aclanthology.org/2023.acl-long.868

Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F., Zhang, T., Li, J., Wang, G.: Gpt-ner: Named entity recognition via large language models (2023). https://doi.org/10.48550/arXiv.2304.10428

2023

Later among the works it cites.

Xu, D., Chen, W., Peng, W., Zhang, C., Xu, T., Zhao, X., Wu, X., Zheng, Y., Chen, E.: Large language models for generative information extraction: A survey. ArXiv (2023). https://doi.org/10.48550/arxiv.2312.17617

2023

Later among the works it cites.

Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., Wang, H.: Retrieval-augmented generation for large language models: A survey (2024). https://doi.org/10.48550/arXiv.2312.10997

2024

Closest in time.

Xie, T., Li, Q., Zhang, Y., Liu, Z., Wang, H.: Self-improving for zero-shot named entity recognition with large language models (2024). https://doi.org/10.48550/arxiv.2311.08921

2024

Closest in time.