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This study proposes a novel hybrid retrieval strategy for Retrieval-Augmented Generation (RAG) that integrates cosine similarity and cosine distance measures to improve retrieval performance, particularly for sparse data.
S. Banerjee and A. Lavie, “METEOR: An automatic metric for MT evaluation with improved correlation with human judgments,” in Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization , J. Goldstein, A. Lavie, C.-Y. Lin, and C. Voss, Eds. Ann Arbor, Michigan: Association for Computational Linguistics, Jun. 2005, pp. 65–72. [Online]. Available: https://aclanthology.org/W05-0909
2005
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S. Robertson and H. Zaragoza, “The probabilistic relevance framework: Bm25 and beyond,” Found. Trends Inf. Retr. , vol. 3, no. 4, p. 333–389, apr 2009. [Online]. Available: https://doi.org/10.1561/1500000019
2009
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V. Boteva, D. Gholipour, A. Sokolov, and S. Riezler, “A full-text learning to rank dataset for medical information retrieval,” in Advances in Information Retrieval , N. Ferro, F. Crestani, M.-F. Moens, J. Mothe, F. Silvestri, G. M. Di Nunzio, C. Hauff, and G. Silvello, Eds. Cham: Springer International Publishing, 2016, pp. 716–722. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-319-30671-1_58
2016
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D. Chen, A. Fisch, J. Weston, and A. Bordes, “Reading Wikipedia to answer open-domain questions,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , R. Barzilay and M.-Y. Kan, Eds. Vancouver, Canada: Association for Computational Linguistics, Jul. 2017, pp. 1870–1879. [Online]. Available: https://aclanthology.org/P17-1171
2017
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F. Petroni, T. Rocktäschel, P. Lewis, A. Bakhtin, Y. Wu, A. H. Miller, and S. Riedel, “Language models as knowledge bases?” 2019
2019
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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 , ser. NIPS ’20. Red Hook, NY, USA: Curran Associates Inc., 2020
2020
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D. Wadden, S. Lin, K. Lo, L. L. Wang, M. van Zuylen, A. Cohan, and H. Hajishirzi, “Fact or fiction: Verifying scientific claims,” in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , B. Webber, T. Cohn, Y. He, and Y. Liu, Eds. Online: Association for Computational Linguistics, Nov. 2020, pp. 7534–7550. [Online]. Available: https://aclanthology.org/2020.emnlp-main.609
2020
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E. Akyurek, T. Bolukbasi, F. Liu, B. Xiong, I. Tenney, J. Andreas, and K. Guu, “Towards tracing knowledge in language models back to the training data,” in Findings of the Association for Computational Linguistics: EMNLP 2022 , Y. Goldberg, Z. Kozareva, and Y. Zhang, Eds. Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec. 2022, pp. 2429–2446. [Online]. Available: https://aclanthology.org/2022.findings-emnlp.180
2022
Cited alongside, same era.
R. Y. Pang, A. Parrish, N. Joshi, N. Nangia, J. Phang, A. Chen, V. Padmakumar, J. Ma, J. Thompson, H. He, and S. Bowman, “QuALITY: Question answering with long input texts, yes!” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , M. Carpuat, M.-C. de Marneffe, and I. V. Meza Ruiz, Eds. Seattle, United States: Association for Computational Linguistics, Jul. 2022, pp. 5336–5358. [Online]. Available: https://aclanthology.org/2022.naacl-main.391
2022
Cited alongside, same era.
H. Steck, C. Ekanadham, and N. Kallus, “Is cosine-similarity of embeddings really about similarity?” in Companion Proceedings of the ACM on Web Conference 2024 , ser. WWW ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 887–890. [Online]. Available: https://doi.org/10.1145/3589335.3651526
2024
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2024
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2024
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N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the middle: How language models use long contexts,” Transactions of the Association for Computational Linguistics , vol. 12, pp. 157–173, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:259360665
2023
Cited alongside, same era.
P. Damodaran, “FlashRank, Lightest and Fastest 2nd Stage Reranker for search pipelines.” Dec. 2023. [Online]. Available: https://github.com/PrithivirajDamodaran/FlashRank
2023
Cited alongside, same era.
2023
Cited alongside, same era.
K. A. Hambarde and H. Proença, “Information retrieval: Recent advances and beyond,” IEEE Access , vol. 11, pp. 76 581–76 604, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10184013
2023
Cited alongside, same era.
A. Purwar and R. Sundar, “Keyword augmented retrieval: Novel framework for information retrieval integrated with speech interface,” in Proceedings of the Third International Conference on AI-ML Systems , ser. AIMLSystems ’23. New York, NY, USA: Association for Computing Machinery, 2024. [Online]. Available: https://doi.org/10.1145/3639856.3639916
2024
Cited alongside, same era.
T. Aarsen, “Spanmarker.” [Online]. Available: https://github.com/tomaarsen/SpanMarkerNER
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C. AI, “Deepeval: The llm evaluation framework,” https://github.com/confident-ai/deepeval , 2024
2024
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2024
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D. Jin, E. Pan, N. Oufattole, W.-H. Weng, H. Fang, and P. Szolovits, “What disease does this patient have? a large-scale open domain question answering dataset from medical exams,” Applied Sciences , vol. 11, no. 14, 2021. [Online]. Available: https://www.mdpi.com/2076-3417/11/14/6421
2076
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