Using ChatGPT for Entity Matching

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Primpeli, A., Bizer, C.: Profiling Entity Matching Benchmark Tasks. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. pp. 3101–3108 (2020)

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Barlaug, N., Gulla, J.A.: Neural Networks for Entity Matching: A Survey. ACM Transactions on Knowledge Discovery from Data 15

2021

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Zhao, Z., Wallace, E., Feng, S., Klein, D., Singh, S.: Calibrate Before Use: Improving Few-Shot Performance of Language Models. In: Proceedings of the 38th International Conference on Machine Learning. pp. 12697–12706 (2021)

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2021

Cited alongside, same era.

Akbarian Rastaghi, M., Kamalloo, E., Rafiei, D.: Probing the Robustness of Pre-trained Language Models for Entity Matching. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. pp. 3786–3790 (2022)

2022

Cited alongside, same era.

Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., et al.: What Makes Good In-Context Examples for GPT-3? In: Proceedings of Deep Learning Inside Out: The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures. pp. 100–114. Association for Computational Linguistics (2022)

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Narayan, A., Chami, I., Orr, L., Ré, C.: Can Foundation Models Wrangle Your Data? Proceedings of the VLDB Endowment 16

2022

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., et al.: Emergent Abilities of Large Language Models. Transactions on Machine Learning Research (2022)

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Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., et al.: Pre-Train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing. ACM Computing Surveys 55

2023

Closest in time.

Peeters, R., Der, R.C., Bizer, C.: WDC Products: A Multi-Dimensional Entity Matching Benchmark. arXiv preprint arXiv:2301.09521 (2023)

Original

2023

Closest in time.

Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., et al.: A Survey of Large Language Models. arXiv preprint arXiv:2303.18223 (2023)

Original

2023

Closest in time.