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Large Language Models (LLMs) like ChatGPT and Llama have revolutionized natural language processing and search engine dynamics.
K. Pearson, “Liii. on lines and planes of closest fit to systems of points in space,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science , vol. 2, no. 11, pp. 559–572, 1901. [Online]. Available: https://doi.org/10.1080/14786440109462720
1901
Earlier work this paper cites.
1909
Earlier work this paper cites.
H. Hotelling, “Analysis of a complex of statistical variables into principal components.” Journal of educational psychology , vol. 24, no. 6, p. 417, 1933
1933
Earlier work this paper cites.
E. P. Markatos, “On caching search engine query results,” Computer Communications , vol. 24, no. 2, pp. 137–143, 2001
2001
Earlier work this paper cites.
P. C. Saraiva, E. Silva de Moura, N. Ziviani, W. Meira, R. Fonseca, and B. Ribeiro-Neto, “Rank-preserving two-level caching for scalable search engines,” in Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval , 2001, pp. 51–58
2001
Earlier work this paper cites.
Y. Xie and D. O’Hallaron, “Locality in search engine queries and its implications for caching,” in Proceedings. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies , vol. 3. IEEE, 2002, pp. 1238–1247
2002
Earlier work this paper cites.
R. Lempel and S. Moran, “Predictive caching and prefetching of query results in search engines,” in Proceedings of the 12th international conference on World Wide Web , 2003, pp. 19–28
2003
Earlier work this paper cites.
S. Podlipnig and L. Böszörmenyi, “A survey of web cache replacement strategies,” ACM Computing Surveys (CSUR) , vol. 35, no. 4, pp. 374–398, 2003
2003
Earlier work this paper cites.
R. Baeza-Yates and F. Saint-Jean, “A three level search engine index based in query log distribution,” in String Processing and Information Retrieval: 10th International Symposium, SPIRE 2003, Manaus, Brazil, October 8-10, 2003. Proceedings 10 . Springer, 2003, pp. 56–65
2003
Earlier work this paper cites.
X. Long and T. Suel, “Three-level caching for efficient query processing in large web search engines,” in Proceedings of the 14th international conference on World Wide Web , 2005, pp. 257–266
2005
Earlier work this paper cites.
T. Fagni, R. Perego, F. Silvestri, and S. Orlando, “Boosting the performance of web search engines: Caching and prefetching query results by exploiting historical usage data,” ACM Transactions on Information Systems (TOIS) , vol. 24, no. 1, pp. 51–78, 2006
2006
Earlier work this paper cites.
R. Baeza-Yates, A. Gionis, F. Junqueira, V. Murdock, V. Plachouras, and F. Silvestri, “The impact of caching on search engines,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval , 2007, pp. 183–190
2007
Earlier work this paper cites.
J. Zhang, X. Long, and T. Suel, “Performance of compressed inverted list caching in search engines,” in Proceedings of the 17th international conference on World Wide Web , 2008, pp. 387–396
2008
Earlier work this paper cites.
I. T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments,” Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences , vol. 374, no. 2065, p. 20150202, 2016
2016
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) , K. Inui, J. Jiang, V. Ng, and X. Wan, Eds. Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 3982–3992. [Online]. Available: https://aclanthology.org/D19-1410
2019
Earlier work this paper cites.
K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu, “Mpnet: Masked and permuted pre-training for language understanding,” Advances in Neural Information Processing Systems , vol. 33, pp. 16 857–16 867, 2020
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, “Federated optimization in heterogeneous networks,” Proceedings of Machine learning and systems , vol. 2, pp. 429–450, 2020
2020
Cited alongside, same era.
H. Wang, M. Yurochkin, Y. Sun, D. Papailiopoulos, and Y. Khazaeni, “Federated learning with matched averaging,” in International Conference on Learning Representations , 2020. [Online]. Available: https://openreview.net/forum?id=BkluqlSFDS
2020
Cited alongside, same era.
V. Karpukhin, B. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-t. Yih, “Dense passage retrieval for open-domain question answering,” 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. 6769–6781. [Online]. Available: https://aclanthology.org/2020.emnlp-main.550
“Learning human actions on computer applications,” https://www.rabbit.tech/research , (Accessed on 01/19/2024)
2024
Closest in time.
“Arc max is the popular browser’s new suite of ai tools - the verge,” https://www.theverge.com/2023/10/3/23898907/arc-max-ai-browser-mac-ios , (Accessed on 01/19/2024)
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
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2020
Cited alongside, same era.
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimization,” Advances in neural information processing systems , vol. 33, pp. 7611–7623, 2020
2020
Cited alongside, same era.
D. Avdiukhin and S. Kasiviswanathan, “Federated learning under arbitrary communication patterns,” in International Conference on Machine Learning . PMLR, 2021, pp. 425–435
2021
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 730–27 744, 2022
2022
Cited alongside, same era.
J. Ni, G. Hernandez Abrego, N. Constant, J. Ma, K. Hall, D. Cer, and Y. Yang, “Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models,” in Findings of the Association for Computational Linguistics: ACL 2022 , S. Muresan, P. Nakov, and A. Villavicencio, Eds. Dublin, Ireland: Association for Computational Linguistics, May 2022, pp. 1864–1874. [Online]. Available: https://aclanthology.org/2022.findings-acl.146
2022
Cited alongside, same era.
2023
Cited alongside, same era.
E. Frantar, S. Ashkboos, T. Hoefler, and D. Alistarh, “OPTQ: accurate quantization for generative pre-trained transformers,” in The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023 . OpenReview.net, 2023. [Online]. Available: https://openreview.net/pdf?id=tcbBPnfwxS
2023
Cited alongside, same era.
B. Zhu, Y. Sheng, L. Zheng, C. Barrett, M. Jordan, and J. Jiao, “Towards optimal caching and model selection for large model inference,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=gd20oaZqqF
2023
Cited alongside, same era.
F. Bang, “Gptcache: An open-source semantic cache for llm applications enabling faster answers and cost savings,” in Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023) , 2023, pp. 212–218
2023
Cited alongside, same era.
2024
Closest in time.
“Perplexity pro,” https://www.perplexity.ai/pro , (Accessed on 03/01/2024)
2024
Closest in time.
“OpenAI Pricing,” https://openai.com/pricing , (Accessed on 01/19/2024)
2024
Closest in time.
“Federated learning: Collaborative machine learning without centralized training data – google research blog,” https://blog.research.google/2017/04/federated-learning-collaborative.html , (Accessed on 04/01/2024)
2024
Closest in time.
G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, H. Alobeidli, A. Cappelli, B. Pannier, E. Almazrouei, and J. Launay, “The refinedweb dataset for falcon llm: Outperforming curated corpora with web data only,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
X. Wang, Q. Le, A. F. Khan, J. Ding, and A. Anwar, “Icl: An incentivized collaborative learning framework,” in 2024 IEEE International Conference on Big Data (BigData) , 2024, pp. 94–103
2024
Closest in time.
“zilliztech/gptcache: Semantic cache for llms. fully integrated with langchain and llama_index.” https://github.com/zilliztech/gptcache , (Accessed on 03/03/2024)
2024
Closest in time.
“Gptcache/examples/benchmark at main · zilliztech/gptcache,” https://github.com/zilliztech/GPTCache/tree/main/examples/benchmark , (Accessed on 03/04/2024)
2024
Closest in time.
A. F. Khan, A. A. Khan, A. M. Abdelmoniem, S. Fountain, A. R. Butt, and A. Anwar, “Float: Federated learning optimizations with automated tuning,” in Proceedings of the Nineteenth European Conference on Computer Systems , ser. EuroSys ’24. New York, NY, USA: Association for Computing Machinery, 2024, p. 200–218. [Online]. Available: https://doi.org/10.1145/3627703.3650081
2024
Closest in time.
“Diskcache: Disk backed cache — diskcache 5.6.1 documentation,” https://grantjenks.com/docs/diskcache/ , (Accessed on 04/01/2024)
2024
Closest in time.
“Sizing guide - nvidia docs,” https://docs.nvidia.com/ai-enterprise/workflows-generative-ai/0.1.0/sizing-guide.html , (Accessed on 01/25/2024)
2024
Closest in time.
“[user] embedding doesn’t seem to work? · issue #899 · ggerganov/llama.cpp,” https://github.com/ggerganov/llama.cpp/issues/899 , (Accessed on 01/18/2024)
2024
Closest in time.
W. Gill, A. Anwar, and M. A. Gulzar, “TraceFL: Interpretability-Driven Debugging in Federated Learning via Neuron Provenance,” in 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE) . IEEE, 2025
2025
Closest in time.