Fetching the paper…
Reading the bibliography…
Retrieval-Augmented Generation (RAG) has been empirically shown to enhance the performance of large language models (LLMs) in knowledge-intensive domains such as healthcare, finance, and legal contexts.
S. E. Robertson, S. Walker, S. Jones, M. M. Hancock-Beaulieu, M. Gatford et al
1995
Earlier work this paper cites.
O. Ledoit and M. Wolf, “Honey, i shrunk the sample covariance matrix,” UPF economics and business working paper
2003
Earlier work this paper cites.
P. J. Bickel, B. Li, A. B. Tsybakov, S. A. van de Geer, B. Yu, T. Valdés, C. Rivero, J. Fan, and A. van der Vaart, “Regularization in statistics,” Test
2006
Earlier work this paper cites.
S. Robertson, H. Zaragoza et al
2009
Earlier work this paper cites.
P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck, “Learning deep structured semantic models for web search using clickthrough data,” in Proceedings of the 22nd ACM international conference on Information & Knowledge Management
2013
Earlier work this paper cites.
T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, and L. Deng, “Ms marco: A human-generated machine reading comprehension dataset,” 2016
2016
Earlier work this paper cites.
P. Voigt and A. Von dem Bussche, “The eu general data protection regulation (gdpr),” A Practical Guide, 1st Ed., Cham: Springer International Publishing
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
R. Nogueira and K. Cho, “Passage re-ranking with bert,” arXiv preprint arXiv:1901.04085
2019
Earlier work this paper cites.
T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. Parikh, C. Alberti, D. Epstein, I. Polosukhin, J. Devlin, K. Lee et al
2019
Earlier work this paper cites.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel et al
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning
2020
Earlier work this paper cites.
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
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. B. Van Den Driessche, J.-B. Lespiau, B. Damoc, A. Clark et al
2022
Cited alongside, same era.
2022
Cited alongside, same era.
U. Alon, F. Xu, J. He, S. Sengupta, D. Roth, and G. Neubig, “Neuro-symbolic language modeling with automaton-augmented retrieval,” in International Conference on Machine Learning
2022
Cited alongside, same era.
2022
Cited alongside, same era.
O. Bodnar, T. Bodnar, and N. Parolya, “Recent advances in shrinkage-based high-dimensional inference,” Journal of Multivariate Analysis
O. Ram, Y. Levine, I. Dalmedigos, D. Muhlgay, A. Shashua, K. Leyton-Brown, and Y. Shoham, “In-context retrieval-augmented language models,” Transactions of the Association for Computational Linguistics
2023
Later among the works it cites.
Q. Jin, W. Kim, Q. Chen, D. C. Comeau, L. Yeganova, W. J. Wilbur, and Z. Lu, “Medcpt: Contrastive pre-trained transformers with large-scale pubmed search logs for zero-shot biomedical information retrieval,” Bioinformatics
2023
Later among the works it cites.
S. Tian, Q. Jin, L. Yeganova, P.-T. Lai, Q. Zhu, X. Chen, Y. Yang, Q. Chen, W. Kim, D. C. Comeau et al
2024
Closest in time.
W. Hersh, “Search still matters: information retrieval in the era of generative ai,” Journal of the American Medical Informatics Association
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
L. N. Trefethen and D. Bau, Numerical linear algebra
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. J. Thirunavukarasu, D. S. J. Ting, K. Elangovan, L. Gutierrez, T. F. Tan, and D. S. W. Ting, “Large language models in medicine,” Nature medicine
2023
Cited alongside, same era.
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. J. Bang, A. Madotto, and P. Fung, “Survey of hallucination in natural language generation,” ACM Computing Surveys
2023
Cited alongside, same era.
L. Loukas, I. Stogiannidis, O. Diamantopoulos, P. Malakasiotis, and S. Vassos, “Making llms worth every penny: Resource-limited text classification in banking,” in Proceedings of the Fourth ACM International Conference on AI in Finance
2023
Cited alongside, same era.
Y.-A. Liu, R. Zhang, J. Guo, M. de Rijke, W. Chen, Y. Fan, and X. Cheng, “Black-box adversarial attacks against dense retrieval models: A multi-view contrastive learning method,” in Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
2023
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
G. Wang, X. Xian, J. Srinivasa, A. Kundu, X. Bi, M. Hong, and J. Ding, “Demystifying poisoning backdoor attacks from a statistical perspective,” International Conference on Learning Representations
2024
Closest in time.
W. X. Zhao, J. Liu, R. Ren, and J.-R. Wen, “Dense text retrieval based on pretrained language models: A survey,” ACM Transactions on Information Systems
2024
Closest in time.
2024
Closest in time.
J. Miao, C. Thongprayoon, S. Suppadungsuk, O. A. Garcia Valencia, and W. Cheungpasitporn, “Integrating retrieval-augmented generation with large language models in nephrology: advancing practical applications,” Medicina
2024
Closest in time.
U. Butler, “Open australian legal corpus (2024),” 2024
2024
Closest in time.
M. Douze, A. Guzhva, C. Deng, J. Johnson, G. Szilvasy, P.-E. Mazaré, M. Lomeli, L. Hosseini, and H. Jégou, “The faiss library,” 2024
2024
Closest in time.
X. Wang, Q. Le, A. Ahmed, E. Diao, Y. Zhou, N. Baracaldo, J. Ding, and A. Anwar, “MAP: Multi-human-value alignment palette,” International Conference on Learning Representations (Oral Presentation)
2025
Closest in time.
Q. Le, E. Diao, Z. Wang, X. Wang, J. Ding, L. Yang, and A. Anwar, “Probe Pruning: Accelerating LLMs Through Dynamic Pruning via Model-Probing,” in Proceedings of the International Conference on Learning Representations
2025
Closest in time.