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SciRepEval: A Multi-Format Benchmark for Scientific Document Representations

Singh, Amanpreet, D’Arcy, Mike, Cohan, Arman, Downey, Doug, Feldman, Sergey·2023·Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
DOI 10.18653/v1/2023.emnlp-main.338
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Figure 1 Generating multi-format embeddings. A task format is either associated with a control code appended to the input, or adapter blocks attached to the model.
Figure 1 Generating multi-format embeddings. A task format is either associated with a control code appended to the input, or adapter blocks attached to the model.CC-BY · From the PDF
Figure 2 Correlations of model performances between tasks in SciRepEval.
Figure 2 Correlations of model performances between tasks in SciRepEval.CC-BY · From the PDF

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Metadata

Authors
Singh, A., D’Arcy, M., Cohan, A., Downey, D., & Feldman, S.
Venue
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Publisher
Association for Computational Linguistics
Published
2023
Pages
5548-5566
DOI
10.18653/v1/2023.emnlp-main.338
Cited by
67
References
10

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CoChat. (2026). Citation check: SciRepEval: A Multi-Format Benchmark for Scientific Document Representations. https://app.cochat.ai/papers/10.18653/v1/2023.emnlp-main.338
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Singh, A., D’Arcy, M., Cohan, A., Downey, D., & Feldman, S. (2023). SciRepEval: A Multi-Format Benchmark for Scientific Document Representations. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2023.emnlp-main.338