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Retrieval-Augmented Generation (RAG) has recently gained traction in natural language processing.
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Isabelle, P., Charniak, E., Lin, D. (eds.) Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. pp. 311–318. Association for Computational Linguistics, Philadelphia, Pennsylvania, USA (Jul 2002). https://doi.org/10.3115/1073083.1073135, https://aclanthology.org/P02-1040
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Shahabi, C., Kolahdouzan, M.R., Sharifzadeh, M.: A road network embedding technique for k-nearest neighbor search in moving object databases. In: Proceedings of the 10th ACM international symposium on advances in geographic information systems. pp. 94–100 (2002)
2002
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Ramos, J., et al.: Using tf-idf to determine word relevance in document queries. In: Proceedings of the first instructional conference on machine learning. vol. 242, pp. 29–48. Citeseer (2003)
2003
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Khattab, O., Zaharia, M.: Colbert: Efficient and effective passage search via contextualized late interaction over bert (Apr 2020). https://doi.org/10.48550/ARXIV.2004.12832
2004
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Lin, C.Y.: ROUGE: A package for automatic evaluation of summaries. In: Text Summarization Branches Out. pp. 74–81. Association for Computational Linguistics, Barcelona, Spain (Jul 2004), https://aclanthology.org/W04-1013
2004
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2005
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Robertson, S., Zaragoza, H., et al.: The probabilistic relevance framework: Bm25 and beyond. Foundations and Trends® in Information Retrieval 3
2009
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Lahitani, A.R., Permanasari, A.E., Setiawan, N.A.: Cosine similarity to determine similarity measure: Study case in online essay assessment. In: 2016 4th International Conference on Cyber and IT Service Management. pp. 1–6 (2016). https://doi.org/10.1109/CITSM.2016.7577578
2016
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need (Jun 2017). https://doi.org/10.48550/ARXIV.1706.03762
2017
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Gottschalk, S., Demidova, E.: Eventkg: A multilingual event-centric temporal knowledge graph (Apr 2018). https://doi.org/10.48550/ARXIV.1804.04526
2018
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Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: a large-scale dataset for fact extraction and VERification. In: NAACL-HLT (2018)
2018
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Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W.W., Salakhutdinov, R., Manning, C.D.: HotpotQA: A dataset for diverse, explainable multi-hop question answering. In: Conference on Empirical Methods in Natural Language Processing (EMNLP) (2018)
2018
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Zhang, S., Liu, X., Liu, J., Gao, J., Duh, K., Van Durme, B.: Record: Bridging the gap between human and machine commonsense reading comprehension (Oct 2018). https://doi.org/10.48550/ARXIV.1810.12885
2018
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (Jun 2019). https://doi.org/10.18653/v1/N19-1423, https://aclanthology.org/N19-1423
2019
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Dinan, E., Roller, S., Shuster, K., Fan, A., Auli, M., Weston, J.: Wizard of Wikipedia: Knowledge-powered conversational agents. In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)
2019
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Fisch, A., Talmor, A., Jia, R., Seo, M., Choi, E., Chen, D.: MRQA 2019 shared task: Evaluating generalization in reading comprehension. In: Fisch, A., Talmor, A., Jia, R., Seo, M., Choi, E., Chen, D. (eds.) Proceedings of the 2nd Workshop on Machine Reading for Question Answering. pp. 1–13. Association for Computational Linguistics, Hong Kong, China (Nov 2019). https://doi.org/10.18653/v1/D19-5801, https://aclanthology.org/D19-5801
2019
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Johnson, J., Douze, M., Jégou, H.: Billion-scale similarity search with GPUs. IEEE Transactions on Big Data 7
2019
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Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., Toutanova, K., Jones, L., Kelcey, M., Chang, M.W., Dai, A.M., Uszkoreit, J., Le, Q., Petrov, S.: Natural questions: A benchmark for question answering research. Transactions of the Association for Computational Linguistics 7
2019
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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1
2019
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Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: SuperGLUE: A stickier benchmark for general-purpose language understanding systems. arXiv preprint 1905.00537 (2019)
2019
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Zhang, K., Zhang, H., Liu, Q., Zhao, H., Zhu, H., Chen, E.: Interactive attention transfer network for cross-domain sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 5773–5780 (2019)
2019
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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: Proceedings of the 34th International Conference on Neural Information Processing Systems. pp. 9459–9474. NIPS’20, Curran Associates Inc., Red Hook, NY, USA (Dec 2020)
2020
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Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: Evaluating Text Generation with BERT. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net (2020), https://openreview.net/forum?id=SkeHuCVFDr
2020
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2021
Earlier work this paper cites.
Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., De Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., Maillard, J., Plachouras, V., Rocktäschel, T., Riedel, S.: KILT: a benchmark for knowledge intensive language tasks. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 2523–2544. Association for Computational Linguistics, Online (Jun 2021). https://doi.org/10.18653/v1/2021.naacl-main.200, https://aclanthology.org/2021.naacl-main.200
2021
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Zhang, K., Liu, Q., Qian, H., Xiang, B., Cui, Q., Zhou, J., Chen, E.: Eatn: An efficient adaptive transfer network for aspect-level sentiment analysis. IEEE Transactions on Knowledge and Data Engineering 35
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Huang, J., Shao, H., Chang, K.C.C., Xiong, J., Hwu, W.m.: Understanding jargon: Combining extraction and generation for definition modeling. In: Proceedings of EMNLP (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T.L., Cao, Y., Narasimhan, K.: Tree of Thoughts: Deliberate problem solving with large language models (2023)
2023
Later among the works it cites.
Zhang, Q., Chen, S., Xu, D., Cao, Q., Chen, X., Cohn, T., Fang, M.: A Survey for Efficient Open Domain Question Answering. 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. 14447–14465. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https://doi.org/10.18653/v1/2023.acl-long.808, https://aclanthology.org/2023.acl-long.808
2023
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2022
Cited alongside, same era.
Sai, A.B., Mohankumar, A.K., Khapra, M.M.: A survey of evaluation metrics used for nlg systems. ACM Computing Surveys (CSUR) 55
2022
Cited alongside, same era.
Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E.H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., Fedus, W.: Emergent abilities of large language models (Jun 2022). https://doi.org/10.48550/ARXIV.2206.07682
2022
Cited alongside, same era.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., Zhou, D.: Chain-of-thought prompting elicits reasoning in large language models (Jan 2022). https://doi.org/10.48550/ARXIV.2201.11903
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Podstawski, M., Gianinazzi, L., Gajda, J., Lehmann, T., Niewiadomski, H., Nyczyk, P., Hoefler, T.: Graph of thoughts: Solving elaborate problems with large language models. Proceedings of the AAAI Conference on Artificial Intelligence 2024 (AAAI’24) (Aug 2023). https://doi.org/10.48550/ARXIV.2308.09687
2023
Cited alongside, same era.
Blagojevic, V.: Enhancing RAG Pipelines in Haystack: Introducing DiversityRanker and LostInTheMiddleRanker (Aug 2023), https://towardsdatascience.com/enhancing-rag-pipelines-in-haystack-45f14e2bc9f5
2023
Cited alongside, same era.
Chen, J., Lin, H., Han, X., Sun, L.: Benchmarking large language models in retrieval-augmented generation (Sep 2023). https://doi.org/10.48550/ARXIV.2309.01431
2023
Cited alongside, same era.
Zhang, Y., Khalifa, M., Logeswaran, L., Lee, M., Lee, H., Wang, L.: Merging Generated and Retrieved Knowledge for Open-Domain QA. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 4710–4728. Association for Computational Linguistics, Singapore (Dec 2023). https://doi.org/10.18653/v1/2023.emnlp-main.286, https://aclanthology.org/2023.emnlp-main.286
2023
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Zheng, L., Chiang, W.L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E.P., Zhang, H., Gonzalez, J.E., Stoica, I.: Judging llm-as-a-judge with mt-bench and chatbot arena (Jun 2023). https://doi.org/10.48550/ARXIV.2306.05685
2023
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Zhou, Y., Lin, X., Zhang, X., Wang, M., Jiang, G., Lu, H., Wu, Y., Zhang, K., Yang, Z., Wang, K., Sui, Y., Jia, F., Tang, Z., Zhao, Y., Zhang, H., Yang, T., Chen, W., Mao, Y., Li, Y., Bao, D., Li, Y., Liao, H., Liu, T., Liu, J., Guo, J., Zhao, X., WEI, Y., Qian, H., Liu, Q., Wang, X., Kin, W., Chan, Li, C., Li, Y., Yang, S., Yan, J., Mou, C., Han, S., Jin, W., Zhang, G., Zeng, X.: On the opportunities of green computing: A survey (Nov 2023)
2023
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2024
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Barnett, S., Kurniawan, S., Thudumu, S., Brannelly, Z., Abdelrazek, M.: Seven failure points when engineering a retrieval augmented generation system (Jan 2024). https://doi.org/10.48550/ARXIV.2401.05856
2024
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Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., et al.: A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology 15
2024
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Cuconasu, F., Trappolini, G., Siciliano, F., Filice, S., Campagnano, C., Maarek, Y., Tonellotto, N., Silvestri, F.: The power of noise: Redefining retrieval for rag systems (Jan 2024). https://doi.org/10.48550/ARXIV.2401.14887
2024
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Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.E., Lomeli, M., Hosseini, L., Jégou, H.: The faiss library (2024)
2024
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DuckDuckGo: DuckDuckGo — Privacy, simplified. (2024), https://duckduckgo.com//home
2024
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2024
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Google: Programmable Search Engine | Google for Developers (2024), https://developers.google.com/custom-search
2024
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Huang, Y., Huang, J.: A survey on retrieval-augmented text generation for large language models (Apr 2024). https://doi.org/10.48550/ARXIV.2404.10981
2024
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Lyu, Y., Li, Z., Niu, S., Xiong, F., Tang, B., Wang, W., Wu, H., Liu, H., Xu, T., Chen, E., Luo, Y., Cheng, P., Deng, H., Wang, Z., Lu, Z.: Crud-rag: A comprehensive chinese benchmark for retrieval-augmented generation of large language models (Jan 2024). https://doi.org/10.48550/ARXIV.2401.17043
2024
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Rosset, C., Chung, H.L., Qin, G., Chau, E.C., Feng, Z., Awadallah, A., Neville, J., Rao, N.: Researchy questions: A dataset of multi-perspective, decompositional questions for llm web agents (Feb 2024). https://doi.org/10.48550/ARXIV.2402.17896
2024
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Tang, Y., Yang, Y.: Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries (Jan 2024). https://doi.org/10.48550/ARXIV.2401.15391
2024
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Wang, S., Khramtsova, E., Zhuang, S., Zuccon, G.: Feb4rag: Evaluating federated search in the context of retrieval augmented generation (Feb 2024). https://doi.org/10.48550/ARXIV.2402.11891
2024
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Wang, S., Liu, J., Song, S., Cheng, J., Fu, Y., Guo, P., Fang, K., Zhu, Y., Dou, Z.: Domainrag: A chinese benchmark for evaluating domain-specific retrieval-augmented generation (Jun 2024). https://doi.org/10.48550/ARXIV.2406.05654
2024
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Xiong, G., Jin, Q., Lu, Z., Zhang, A.: Benchmarking retrieval-augmented generation for medicine (Feb 2024). https://doi.org/10.48550/ARXIV.2402.13178
2024
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Xu, Z., Li, Y., Ding, R., Wang, X., Chen, B., Jiang, Y., Zheng, H.T., Lu, W., Xie, P., Huang, F.: Let llms take on the latest challenges! a chinese dynamic question answering benchmark (Feb 2024). https://doi.org/10.48550/ARXIV.2402.19248
2024
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Yu, X., Cheng, H., Liu, X., Roth, D., Gao, J.: ReEval: Automatic hallucination evaluation for retrieval-augmented large language models via transferable adversarial attacks. In: Duh, K., Gomez, H., Bethard, S. (eds.) Findings of the Association for Computational Linguistics: NAACL 2024. pp. 1333–1351. Association for Computational Linguistics, Mexico City, Mexico (Jun 2024), https://aclanthology.org/2024.findings-naacl.85
2024
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