mirror of
https://github.com/harvard-edge/cs249r_book.git
synced 2026-07-16 14:42:29 -05:00
Codex chapter-by-chapter audit findings applied across vol1 and vol2:
prose rewrites where the cited source supported only a narrower or
adjacent claim, plus new audit tooling
(book/tools/scripts/build_citation_reference_packets.py and the
companion workflow doc).
Author-form cleanup pass on top of the audit:
- 19 narrative @key conversions where prose already named the author
(e.g., "Sambasivan et al. describe ... [@sambasivan2021]" -> narrative
"@sambasivan2021 describe ...", rendering "Sambasivan et al. (2021)
describe ...") - removes the citeproc duplication that the
manual-bracket regex did not catch.
- 4 [-@key] suppressed-author conversions for possessive eponyms
(Han et al., Vaswani et al., Patarasuk and Yuan, Linnainmaa).
- 2 [-@key] conversions where the prose names an author/entity that
matches the cited author (Patterson and Hennessy's iron law,
Google's data centers + Google whitepaper).
- 1 Horowitz footnote rewritten to narrative so the inline year
replaces a duplicated "(ISSCC 2014, ...) (Horowitz 2014)" pair.
- 1 narrative @russell2021 anchoring "As Russell argues" that was
previously a bare attribution.
- 1 pre-existing narrative fix for "Graham et al. report ..." in
collective_communication that had no cite on the line.
Pre-commit cleanups landing with this commit:
- subramanya2019diskann: add publisher = Curran Associates (NeurIPS
proceedings, per bib-check rule 3).
- Remove orphaned @misc{Wu2016} GNMT bib entry in vol2; it had no
citations and collided case-insensitively with @inproceedings{wu2016}
(the cited Quantized CNNs paper) under bibtex-tidy's normalization.
- vol1/training/training.qmd: drop the dead UtilizationGap LEGO cell
(gpu_real_tflops_*, cluster_*_tflops_*) and the dead
TrainingModels.{gpt3_gpu_years,gpt3_compute_cost}_str exports plus
their unused upstream values.
- vol2/distributed_training/distributed_training.qmd: anchor the
orphaned [^fn-parameter-server] footnote on the body-prose mention
of "parameter-server systems" rather than deleting the definition.
manual-bracket hook (book-check-refs) green at this tip.
577 lines
19 KiB
BibTeX
577 lines
19 KiB
BibTeX
@inproceedings{abadi2016,
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title = {Deep Learning With Differential Privacy},
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author = {Abadi, Martin and Chu, Andy and Goodfellow, Ian and others},
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year = {2016},
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booktitle = {Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security},
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publisher = {ACM},
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pages = {308--318},
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doi = {10.1145/2976749.2978318},
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url = {https://doi.org/10.1145/2976749.2978318},
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source = {Crossref},
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}
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@inproceedings{agrawal2025,
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title = {Taming {Throughput-Latency} Tradeoff in {LLM} Inference with {Sarathi-Serve}},
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author = {
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Agrawal, Amey and Kedia, Nitin and Panwar, Ashish and Mohan, Jayashree and Kwatra, Nipun and
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Gulavani, Bhargav and Tumanov, Alexey and Ramjee, Ramachandran
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},
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year = {2024},
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month = jul,
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booktitle = {18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)},
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publisher = {USENIX Association},
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pages = {117--134},
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isbn = {978-1-939133-40-3},
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url = {https://www.usenix.org/conference/osdi24/presentation/agrawal},
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}
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@misc{amodei2018ai,
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title = {{AI} and Compute},
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author = {Amodei, Dario and Hernandez, Danny},
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year = {2018},
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url = {https://openai.com/research/ai-and-compute},
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howpublished = {OpenAI Blog},
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}
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@book{barroso2019,
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title = {The Datacenter as a Computer},
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author = {Barroso, Luiz Andr{\'e} and H{\"o}lzle, Urs and Ranganathan, Parthasarathy},
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year = {2018},
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publisher = {Springer International Publishing},
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series = {Synthesis Lectures on Computer Architecture},
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doi = {10.1007/978-3-031-01761-2},
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isbn = {9783031006333, 9783031017612},
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issn = {1935-3235, 1935-3243},
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url = {https://doi.org/10.1007/978-3-031-01761-2},
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subtitle = {Designing Warehouse-Scale Machines},
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source = {Crossref},
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edition = {3rd},
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}
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@article{chowdhery2022palm,
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title = {{PaLM}: Scaling Language Modeling with Pathways},
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author = {Chowdhery, Aakanksha and Narang, Sharan and Devlin, Jacob and others},
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year = {2023},
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journal = {Journal of Machine Learning Research},
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volume = {24},
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number = {240},
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pages = {1--113},
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}
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@article{daly2006,
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title = {A Higher Order Estimate of the Optimum Checkpoint Interval for Restart Dumps},
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author = {Daly, John T.},
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year = {2006},
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journal = {Future Gener. Comput. Syst.},
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volume = {22},
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pages = {303--312},
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doi = {10.1016/j.future.2004.11.016},
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url = {https://doi.org/10.1016/j.future.2004.11.016},
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}
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@inproceedings{dao2022,
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title = {FlashAttention: Fast and Memory-Efficient Exact Attention With IO-Awareness},
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author = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
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year = {2022},
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booktitle = {Advances in Neural Information Processing Systems 35},
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publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)},
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pages = {16344--16359},
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doi = {10.52202/068431-1189},
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url = {https://doi.org/10.52202/068431-1189},
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source = {Crossref},
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}
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@article{dean2012large,
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title = {Large Scale Distributed Deep Networks},
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author = {Dean, Jeffrey and Corrado, Greg S. and Monga, Rajat and others},
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year = {2012},
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journal = {Advances in Neural Information Processing Systems},
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volume = {25},
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}
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@article{dean2013,
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title = {The Tail at Scale},
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author = {Dean, Jeffrey and Barroso, Luiz Andr{\'e}},
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year = {2013},
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journal = {Communications of the ACM},
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publisher = {Association for Computing Machinery (ACM)},
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volume = {56},
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number = {2},
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pages = {74--80},
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doi = {10.1145/2408776.2408794},
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issn = {0001-0782, 1557-7317},
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url = {https://doi.org/10.1145/2408776.2408794},
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source = {Crossref},
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}
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@inproceedings{eisenman2022checknrun,
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title = {Check-N-Run: a Checkpointing System for Training Deep Learning Recommendation Models},
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author = {
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Eisenman, Assaf and Matam, Kiran Kumar and Ingram, Steven and Mudigere, Dheevatsa and
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Krishnamoorthi, Raghuraman and Nair, Krishnakumar and Smelyanskiy, Misha and Annavaram, Murali
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},
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year = {2022},
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booktitle = {
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Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI)
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},
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publisher = {USENIX Association},
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}
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@article{fedus2022switch,
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title = {Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity},
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author = {Fedus, William and Zoph, Barret and Shazeer, Noam},
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year = {2022},
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journal = {Journal of Machine Learning Research},
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volume = {23},
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number = {120},
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pages = {1--39},
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url = {https://www.jmlr.org/papers/v23/21-0998.html},
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}
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@inbook{gholami2022,
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title = {A Survey of Quantization Methods for Efficient Neural Network Inference},
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author = {Gholami, Amir and Kim, Sehoon and Dong, Zhen and others},
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year = {2021},
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journal = {arXiv preprint arXiv:2103.13630},
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booktitle = {Low-Power Computer Vision},
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publisher = {Chapman and Hall/CRC},
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pages = {291--326},
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doi = {10.1201/9781003162810-13},
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isbn = {9781003162810},
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url = {https://doi.org/10.1201/9781003162810-13},
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source = {Crossref},
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}
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@article{han2015deep,
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title = {
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Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and
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Huffman Coding
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},
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author = {Han, Song and Mao, Huizi and Dally, William J.},
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year = {2015},
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journal = {arXiv preprint arXiv:1510.00149},
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}
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@book{hennessy2019architecture,
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title = {Computer Architecture: A Quantitative Approach},
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author = {Hennessy, John L. and Patterson, David A.},
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year = {2019},
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publisher = {Morgan Kaufmann},
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isbn = {978-0128119051},
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edition = {6th},
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}
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@inproceedings{hoffmann2022chinchilla,
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title = {Training Compute-Optimal Large Language Models},
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author = {Hoffmann, Jordan and Borgeaud, Sebastian and Mensch, Arthur and others},
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year = {2022},
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journal = {arXiv preprint arXiv:2203.15556},
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booktitle = {Advances in Neural Information Processing Systems 35},
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publisher = {Neural Information Processing Systems Foundation, Inc. (NeurIPS)},
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pages = {30016--30030},
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doi = {10.52202/068431-2176},
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url = {https://doi.org/10.52202/068431-2176},
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source = {Crossref},
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}
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@inproceedings{isaev2023,
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title = {
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Calculon: A Methodology and Tool for High-Level Co-Design of Systems and Large Language Models
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},
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author = {Isaev, Mikhail and McDonald, Nic and Dennison, Larry and Vuduc, Richard},
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year = {2023},
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booktitle = {
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Proceedings of the International Conference for High Performance Computing, Networking, Storage
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and Analysis
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},
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publisher = {ACM},
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pages = {1--14},
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doi = {10.1145/3581784.3607102},
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url = {https://doi.org/10.1145/3581784.3607102},
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source = {Crossref},
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}
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@inproceedings{jouppi2017,
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title = {In-Datacenter Performance Analysis of a Tensor Processing Unit},
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author = {Jouppi, Norman P. and Young, Cliff and Patil, Nishant and others},
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booktitle = {Proceedings of the 44th Annual International Symposium on Computer Architecture},
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}
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@article{kaplan2020scaling,
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title = {Scaling Laws for Neural Language Models},
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author = {Kaplan, Jared and McCandlish, Sam and Henighan, Tom and others},
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year = {2020},
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journal = {arXiv preprint arXiv:2001.08361},
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}
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@article{korthikanti2023,
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title = {Reducing Activation Recomputation in Large Transformer Models},
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author = {
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Korthikanti, Vijay Anand and Casper, Jared and Lym, Sangkug and McAfee, Lawrence and Andersch,
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Michael and Shoeybi, Mohammad and Catanzaro, Bryan
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},
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year = {2023},
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journal = {Proceedings of Machine Learning and Systems},
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volume = {5},
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url = {
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https://proceedings.mlsys.org/paper_files/paper/2023/hash/80083951326cf5b35e5100260d64ed81-Abstract-mlsys2023.html
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},
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}
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@inproceedings{kwon2023,
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title = {Efficient Memory Management for Large Language Model Serving With PagedAttention},
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author = {Kwon, Woosuk and Li, Zhuohan and Zhuang, Siyuan and others},
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year = {2023},
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booktitle = {Proceedings of the 29th Symposium on Operating Systems Principles},
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publisher = {ACM},
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pages = {611--626},
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doi = {10.1145/3600006.3613165},
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url = {https://doi.org/10.1145/3600006.3613165},
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source = {Crossref},
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}
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@article{leiserson1985,
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title = {Fat-Trees: Universal Networks for Hardware-Efficient Supercomputing},
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author = {Leiserson, Charles E.},
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year = {1985},
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journal = {IEEE Transactions on Computers},
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pages = {892--901},
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url = {https://doi.org/10.1109/tc.1985.6312192},
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source = {Crossref},
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}
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@article{lepikhin2020gshard,
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title = {{GShard}: Scaling Giant Models with Conditional Computation and Automatic Sharding},
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author = {
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Lepikhin, Dmitry and Lee, HyoukJoong and Xu, Yuanzhong and Chen, Dehao and Firat, Orhan and
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Huang, Yanping and Krikun, Maxim and Shazeer, Noam and Chen, Zhifeng
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journal = {arXiv preprint arXiv:2006.16668},
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url = {https://arxiv.org/abs/2006.16668},
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}
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@inproceedings{leviathan2023fast,
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title = {Fast Inference from Transformers via Speculative Decoding},
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author = {Leviathan, Yaniv and Kalman, Matan and Matias, Yossi},
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year = {2023},
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booktitle = {Proceedings of the 40th International Conference on Machine Learning (ICML)},
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publisher = {PMLR},
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url = {https://arxiv.org/abs/2211.17192},
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}
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@article{little1961,
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title = {A Proof for the Queuing Formula: <I>L</i> = \ensuremath{\Lambda}<I>W</i>},
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}
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@article{mattson2020,
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title = {MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance},
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author = {Mattson, Peter and Cheng, Christine and Diamos, Gregory and others},
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journal = {IEEE Micro},
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pages = {8--16},
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source = {Crossref},
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}
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@book{mlsysbook2025,
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title = {
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Machine Learning Systems: Principles and Practices of Engineering Artificially Intelligent
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Systems
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},
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author = {Reddi, Vijay Janapa and others},
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year = {2025},
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publisher = {Harvard University},
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url = {https://mlsysbook.ai},
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}
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@article{mohan2021,
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title = {Analyzing and Mitigating Data Stalls in DNN Training},
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author = {Mohan, Jayashree and Phanishayee, Amar and Raniwala, Ashish and Chidambaram, Vijay},
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year = {2021},
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journal = {Proc. VLDB Endow.},
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publisher = {Association for Computing Machinery (ACM)},
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volume = {14},
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pages = {771--784},
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doi = {10.14778/3446095.3446100},
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issn = {2150-8097},
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url = {https://doi.org/10.14778/3446095.3446100},
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source = {Crossref},
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}
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@article{murray2021tf,
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title = {{tf.data}: A Machine Learning Data Processing Framework},
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author = {Murray, Derek G. and Simsa, Jiri and Klimovic, Ana and Indyk, Ihor},
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year = {2021},
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journal = {Proceedings of the VLDB Endowment},
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publisher = {VLDB Endowment},
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volume = {14},
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number = {12},
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}
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@inproceedings{narayanan2021,
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title = {Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM},
|
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author = {Narayanan, Deepak and Shoeybi, Mohammad and Casper, Jared and others},
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year = {2021},
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booktitle = {
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Proceedings of the International Conference for High Performance Computing, Networking, Storage
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and Analysis
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},
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publisher = {ACM},
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pages = {1--15},
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doi = {10.1145/3458817.3476209},
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url = {https://doi.org/10.1145/3458817.3476209},
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source = {Crossref},
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}
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@misc{nvidia2023h100,
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title = {{NVIDIA H100 Tensor Core GPU} Datasheet},
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author = {{NVIDIA Corporation}},
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year = {2023},
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note = {Accessed: 2024-06-15},
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howpublished = {\url{https://www.nvidia.com/en-us/data-center/h100/}},
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}
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@inproceedings{parashar2019,
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title = {Timeloop: A Systematic Approach to DNN Accelerator Evaluation},
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author = {
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Parashar, Angshuman and Raina, Priyanka and Shao, Yakun Sophia and Chen, Yu-Hsin and Emer, Joel
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and others
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},
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year = {2019},
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booktitle = {2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)},
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publisher = {IEEE},
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pages = {304--315},
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doi = {10.1109/ispass.2019.00042},
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url = {https://doi.org/10.1109/ispass.2019.00042},
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source = {Crossref},
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}
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|
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@article{patarasuk2009,
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title = {Bandwidth optimal all-reduce algorithms for clusters of workstations},
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author = {Patarasuk, Pitch and Yuan, Xin},
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year = {2009},
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journal = {Journal of Parallel and Distributed Computing},
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publisher = {Elsevier},
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volume = {69},
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number = {2},
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pages = {117--124},
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doi = {10.1016/j.jpdc.2008.09.002},
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url = {https://doi.org/10.1016/j.jpdc.2008.09.002},
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}
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@inproceedings{patel2024,
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title = {Splitwise: Efficient Generative LLM Inference Using Phase Splitting},
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author = {
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Patel, Pratyush and Choukse, Esha and Zhang, Chaojie and Shah, Aashaka and Goiri, {\'I}{\~n}igo
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and Maleki, Saeed and Bianchini, Ricardo
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},
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year = {2024},
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booktitle = {2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)},
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publisher = {IEEE},
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pages = {118--132},
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doi = {10.1109/isca59077.2024.00019},
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url = {https://doi.org/10.1109/isca59077.2024.00019},
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source = {Crossref},
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}
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@article{patterson2021carbon,
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title = {Carbon Emissions and Large Neural Network Training},
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author = {Patterson, David and Gonzalez, Joseph and Le, Quoc and others},
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year = {2021},
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journal = {arXiv preprint arXiv:2104.10350},
|
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}
|
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@inproceedings{pope2023llm,
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