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Bibliography

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2026

  1. Abdool, Mustafa, Soumyadip Banerjee, Moutupsi Paul, Do-kyum Kim, Xioawei Liu, Bin Xu, Tracy Yu, et al. 2026. “Applying Embedding-Based Retrieval to Airbnb Search.” arXiv:2601.06873 [cs.IR]. arxiv.org/abs/2601.06873
  2. Liu, Wenhan, Xinyu Ma, Yutao Zhu, Yuchen Li, Daiting Shi, Dawei Yin, and Zhicheng Dou. 2026. “Agentic-r: Learning to Retrieve for Agentic Search.” arXiv:2601.11888 [cs.IR]. arxiv.org/abs/2601.11888

2025

  1. Postek, Krzysztof, Alessandro Zocca, Joaquim Gromicho, and Jeffrey Kantor. 2025. Hands-on Mathematical Optimization with Python. Cambridge: Cambridge University Press. doi.org/10.1017/9781009493512
  2. Belcak, Peter, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, and Pavlo Molchanov. 2025. “Small Language Models Are the Future of Agentic AI.” arXiv:2506.02153 [cs.AI]. arxiv.org/abs/2506.02153
  3. Erturk, Eray, Fahad Kamran, Salar Abbaspourazad, Sean Jewell, Harsh Sharma, Yujie Li, Sinead Williamson, Nicholas J Foti, and Joseph Futoma. 2025. “Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions.” arXiv:2507.00191 [cs.LG]. arxiv.org/abs/2507.00191
  4. Feng, Hao, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, et al. 2025. “Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting.” arXiv preprint arXiv:2505.14059. arxiv.org/abs/2505.14059
  5. Ghosh, Shaona, Barnaby Simkin, Kyriacos Shiarlis, Soumili Nandi, Dan Zhao, Matthew Fiedler, Julia Bazinska, et al. 2025. “A Safety and Security Framework for Real-World Agentic Systems.” arXiv:2511.21990 [cs.LG]. arxiv.org/abs/2511.21990
  6. Gill, Waris, Natalie Isak, and Matthew Dressman. 2025. “Cross-Service Threat Intelligence in LLM Services Using Privacy-Preserving Fingerprints.” arXiv:2509.05608 [cs.CR]. arxiv.org/abs/2509.05608
  7. Team, Granite Vision, Leonid Karlinsky, Assaf Arbelle, Abraham Daniels, Ahmed Nassar, Amit Alfassi, Bo Wu, et al. 2025. “Granite Vision: A Lightweight, Open-Source Multimodal Model for Enterprise Intelligence.” arXiv:2502.09927 [cs.CV]. arxiv.org/abs/2502.09927
  8. Reda, Waleed, Abhinav Jangda, and Krishna Chintalapudi. 2025. “Task Specific Pruning with LLM-sieve: How Many Parameters Does Your Task Really Need?” arXiv:2505.18350 [cs.LG]. arxiv.org/abs/2505.18350
  9. Steiner, Aaron, Ralph Peeters, and Christian Bizer. 2025. “MCP vs RAG vs Nlweb vs HTML: A Comparison of the Effectiveness and Efficiency of Different Agent Interfaces to the Web (Technical Report).” arXiv:2511.23281 [cs.CL]. arxiv.org/abs/2511.23281
  10. Su, Hongjin, Shizhe Diao, Ximing Lu, Mingjie Liu, Jiacheng Xu, Xin Dong, Yonggan Fu, et al. 2025. “ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration.” arXiv:2511.21689 [cs.CL]. arxiv.org/abs/2511.21689
  11. Tong, Chris, Youhe Jiang, Gufeng Chen, Tianyi Zhao, Sibian Lu, Wenjie Qu, Eric Yang, Lynn Ai, and Binhang Yuan. 2025. “Parallax: Efficient LLM Inference Service over Decentralized Environment.” arXiv:2509.26182 [cs.DC]. arxiv.org/abs/2509.26182
  12. Wang, Guan, Jin Li, Yuhao Sun, Xing Chen, Changling Liu, Yue Wu, Meng Lu, Sen Song, and Yasin Abbasi Yadkori. 2025. “Hierarchical Reasoning Model.” arXiv:2506.21734 [cs.AI]. arxiv.org/abs/2506.21734
  13. Wang, Yichuan, Shu Liu, Zhifei Li, Yongji Wu, Ziming Mao, Yilong Zhao, Xiao Yan, et al. 2025. “LEANN: A Low-Storage Vector Index.” arXiv:2506.08276 [cs.DB]. arxiv.org/abs/2506.08276
  14. Weller, Orion, Michael Boratko, Iftekhar Naim, and Jinhyuk Lee. 2025. “On the Theoretical Limitations of Embedding-Based Retrieval.” arXiv:2508.21038 [cs.IR]. arxiv.org/abs/2508.21038
  15. Ye, Yixin, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. 2025. “LIMO: Less Is More for Reasoning.” arXiv:2502.03387 [cs.CL]. arxiv.org/abs/2502.03387

2024

  1. Gross, Carson, Dillon Shaffer, and Matt Revelle. 2024. “Hypermedia Controls: Feral to Formal.” In Proceedings of the 35th ACM Conference on Hypertext and Social Media, 52–64. New York, NY, USA: Association for Computing Machinery. doi.org/10.1145/3648188.3675127
  2. Team, Deep Search. 2024. “Docling Technical Report.” Technical report. doi.org/10.48550/arxiv.2408.09869
  3. Liquet, Benoit, Sarat Moka, and Yoni Nazarathy. 2024. Mathematical Engineering of Deep Learning. CRC Press.
  4. Abdin, Marah, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, et al. 2024. “Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.” arXiv:2404.14219 [cs.CL]. arxiv.org/abs/2404.14219
  5. Allen-Zhu, Zeyuan, and Yuanzhi Li. 2024. “Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws.” arXiv:2404.05405 [cs.CL]. arxiv.org/abs/2404.05405
  6. Vaibhav Gupta, Aaron Villalpando, and Boundary ML team. 2024. “BAML.” github.com/boundaryml/baml
  7. Banerjee, Sourav, Ayushi Agarwal, and Saloni Singla. 2024. “LLMs Will Always Hallucinate, and We Need to Live with This.” arXiv:2409.05746 [stat.ML]. arxiv.org/abs/2409.05746
  8. Chen, Zixiang, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu. 2024. “Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.” arXiv:2401.01335 [cs.LG]. arxiv.org/abs/2401.01335
  9. Cheng, Kewei, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Binxuan Huang, Ruirui Li, Shiyang Li, et al. 2024. “Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of Llms.” arXiv:2408.00114 [cs.AI]. arxiv.org/abs/2408.00114
  10. Dalal, Siddhartha, and Vishal Misra. 2024. “The Matrix: A Bayesian Learning Model for Llms.” arXiv:2402.03175 [cs.LG]. arxiv.org/abs/2402.03175
  11. Das, Sayantan. 2024. “Model Alignment Process.” blog.premai.io/model-alignment-process/
  12. Gao, Yunfan, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, and Haofen Wang. 2024. “Retrieval-Augmented Generation for Large Language Models: A Survey.” arXiv:2312.10997 [cs.CL]. arxiv.org/abs/2312.10997
  13. Gu, Albert, and Tri Dao. 2024. “Mamba: Linear-time Sequence Modeling with Selective State Spaces.” arXiv:2312.00752 [cs.LG]. arxiv.org/abs/2312.00752
  14. Jeong, Soyeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong C. Park. 2024. “Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity.” arXiv:2403.14403 [cs.CL]. arxiv.org/abs/2403.14403
  15. Jurafsky, Daniel, and James H. Martin. 2024. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. web.stanford.edu/~jurafsky/slp3/
  16. Kallini, Julie, Isabel Papadimitriou, Richard Futrell, Kyle Mahowald, and Christopher Potts. 2024. “Mission: Impossible Language Models.” arXiv:2401.06416 [cs.CL]. arxiv.org/abs/2401.06416
  17. Koilia, Nikoletta, and Christoforos Kachris. 2024. “Hardware Acceleration of LLMs: A Comprehensive Survey and Comparison.” arXiv:2409.03384 [cs.AR]. arxiv.org/abs/2409.03384
  18. Lambert, Nathan, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, et al. 2024. “RewardBench: Evaluating Reward Models for Language Modeling.” arXiv:2403.13787 [cs.LG]. arxiv.org/abs/2403.13787
  19. Lee, Jinhyuk, Zhuyun Dai, Xiaoqi Ren, Blair Chen, Daniel Cer, Jeremy R. Cole, Kai Hui, et al. 2024. “Gecko: Versatile Text Embeddings Distilled from Large Language Models.” arXiv:2403.20327 [cs.CL]. arxiv.org/abs/2403.20327
  20. Li, Junyou, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye. 2024. “More Agents Is All You Need.” arXiv:2402.05120 [cs.CL]. arxiv.org/abs/2402.05120
  21. Lin, Yiming, Madelon Hulsebos, Ruiying Ma, Shreya Shankar, Sepanta Zeigham, Aditya G. Parameswaran, and Eugene Wu. 2024. “Towards Accurate and Efficient Document Analytics with Large Language Models.” arXiv:2405.04674 [cs.DB]. arxiv.org/abs/2405.04674
  22. Liu, Ziming, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, and Max Tegmark. 2024. “KAN: Kolmogorov-arnold Networks.” arXiv:2404.19756 [cs.LG]. arxiv.org/abs/2404.19756
  23. Luo, Hongyin, and Wei Sun. 2024. “Addition Is All You Need for Energy-Efficient Language Models.” arXiv:2410.00907 [cs.CL]. arxiv.org/abs/2410.00907
  24. Ma, Xuezhe, Xiaomeng Yang, Wenhan Xiong, Beidi Chen, Lili Yu, Hao Zhang, Jonathan May, Luke Zettlemoyer, Omer Levy, and Chunting Zhou. 2024. “Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length.” arXiv:2404.08801 [cs.LG]. arxiv.org/abs/2404.08801
  25. Miller, D. Lee. 2024. “WordLlama: Recycled Token Embeddings from Large Language Models.” github.com/dleemiller/wordllama
  26. Ren, Liliang, Yang Liu, Yadong Lu, Yelong Shen, Chen Liang, and Weizhu Chen. 2024. “Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling.” arXiv:2406.07522 [id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.']. arxiv.org/abs/2406.07522
  27. Sarmah, Bhaskarjit, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, and Dhagash Mehta. 2024. “HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction.” arXiv:2408.04948 [cs.CL]. arxiv.org/abs/2408.04948
  28. Sarthi, Parth, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D. Manning. 2024. “RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval.” arXiv:2401.18059 [cs.CL]. arxiv.org/abs/2401.18059
  29. Shi, Liang, Zhengju Tang, and Zhi Yang. 2024. “A Survey on Employing Large Language Models for Text-to-SQL Tasks.” arXiv:2407.15186 [cs.CL]. arxiv.org/abs/2407.15186
  30. Tao, Wei, Yucheng Zhou, Wenqiang Zhang, and Yu Cheng. 2024. “MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution.” arXiv:2403.17927 [cs.SE]. arxiv.org/abs/2403.17927
  31. van Kempen, Nicolas, Hyuk-Je Kwon, Dung Tuan Nguyen, and Emery D. Berger. 2024. “It's Not Easy Being Green: On the Energy Efficiency of Programming Languages.” arXiv:2410.05460 [cs.PL]. arxiv.org/abs/2410.05460
  32. Veluri, Bandhav, Malek Itani, Tuochao Chen, Takuya Yoshioka, and Shyamnath Gollakota. 2024. “Look Once to Hear: Target Speech Hearing with Noisy Examples.” arXiv:2405.06289 [cs.SD]. arxiv.org/abs/2405.06289
  33. Wei, Jerry, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Dustin Tran, Daiyi Peng, et al. 2024. “Long-Form Factuality in Large Language Models.” arXiv:2403.18802. arxiv.org/abs/2403.18802
  34. Yang, John, Carlos E. Jimenez, Alexander Wettig, Shunyu Yao, Karthik Narasimhan, and Ofir Press. 2024. “SWE-agent: Agent Computer Interfaces Enable Software Engineering Language Models.” swe-agent.com
  35. You, Keen, Haotian Zhang, Eldon Schoop, Floris Weers, Amanda Swearngin, Jeffrey Nichols, Yinfei Yang, and Zhe Gan. 2024. “Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs.” arXiv:2404.05719 [cs.CV]. arxiv.org/abs/2404.05719
  36. Yuan, Zhengqing, Ruoxi Chen, Zhaoxu Li, Haolong Jia, Lifang He, Chi Wang, and Lichao Sun. 2024. “Mora: Enabling Generalist Video Generation via a Multi-Agent Framework.” arXiv:2403.13248 [cs.CV]. arxiv.org/abs/2403.13248
  37. Yuan, Weizhe, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason Weston. 2024. “Self-Rewarding Language Models.” arXiv:2401.10020 [cs.CL]. arxiv.org/abs/2401.10020
  38. Zhuang, Xiaomin, Yufan Jiang, Qiaozhi He, and Zhihua Wu. 2024. “ChuXin: 1.6B Technical Report.” arXiv:2405.04828 [cs.CL]. arxiv.org/abs/2405.04828

2023

  1. Fernandez, Raul Castro, Aaron J. Elmore, Michael J. Franklin, Sanjay Krishnan, and Chenhao Tan. 2023. “How Large Language Models Will Disrupt Data Management.” Proc. VLDB Endow. 16 (11): 3302–3309. doi.org/10.14778/3611479.3611527
  2. Asai, Akari, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. “Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection.” arXiv:2310.11511 [cs.CL]. arxiv.org/abs/2310.11511
  3. Azevedo, Nádila, Gustavo Aquino, Leonardo Nascimento, Leonardo Camelo, Thiago Figueira, Joel Oliveira, Ingrid Figueiredo, André Printes, Israel Torné, and Carlos Figueiredo. 2023. “A Novel Methodology for Developing Troubleshooting Chatbots Applied to ATM Technical Maintenance Support.” Applied Sciences 13 (11): 6777.
  4. Barrault, Loïc, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, et al. 2023. “SeamlessM4T-Massively Multilingual & Multimodal Machine Translation.” arXiv preprint arXiv:2308.11596. arxiv.org/abs/2308.11596
  5. Belcak, Peter, and Roger Wattenhofer. 2023. “Exponentially Faster Language Modelling.” arXiv:2311.10770 [cs.CL]. arxiv.org/abs/2311.10770
  6. Bilokon, Paul, and Burak Gunduz. 2023. “C++ Design Patterns for Low-Latency Applications Including High-Frequency Trading.” arXiv:2309.04259 [cs.PF]. arxiv.org/abs/2309.04259
  7. Bubeck, Sébastien, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, et al. 2023. “Sparks of Artificial General Intelligence: Early Experiments with GPT-4.”
  8. Bulatov, Aydar, Yuri Kuratov, and Mikhail S. Burtsev. 2023. “Scaling Transformer to 1M Tokens and beyond with RMT.”
  9. Chen, Hailin, Fangkai Jiao, Xingxuan Li, Chengwei Qin, Mathieu Ravaut, Ruochen Zhao, Caiming Xiong, and Shafiq Joty. 2023. “ChatGPT's One-Year Anniversary: Are Open-Source Large Language Models Catching Up?” arXiv:2311.16989 [cs.CL]. arxiv.org/abs/2311.16989
  10. Chen, Li, Penghao Wu, Kashyap Chitta, Bernhard Jaeger, Andreas Geiger, and Hongyang Li. 2023. “End-to-End Autonomous Driving: Challenges and Frontiers.” arXiv:2306.16927 [cs.RO]. arxiv.org/abs/2306.16927
  11. Dai, Damai, Yutao Sun, Li Dong, Yaru Hao, Shuming Ma, Zhifang Sui, and Furu Wei. 2023. “Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.” arXiv:2212.10559 [cs.CL]. arxiv.org/abs/2212.10559
  12. Eldan, Ronen, and Yuanzhi Li. 2023. “TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”
  13. Girdhar, Rohit, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. 2023. “Imagebind: One Embedding Space to Bind Them All.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 15180–15190. arxiv.org/abs/2305.05665
  14. Gunasekar, Suriya, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, et al. 2023. “Textbooks Are All You Need.” arXiv:2306.11644 [cs.CL]. arxiv.org/abs/2306.11644
  15. Idziorek, Joseph, Alex Keyes, Colin Lazier, Somu Perianayagam, Prithvi Ramanathan, James Christopher Sorenson III, Doug Terry, and Akshat Vig. 2023. “Distributed Transactions at Scale in Amazon DynamoDB.” In 2023 USENIX Annual Technical Conference (USENIX ATC 23), 705–717.
  16. Jiang, Albert Q., Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, et al. 2023. “Mistral 7B.” arXiv:2310.06825 [cs.CL]. arxiv.org/abs/2310.06825
  17. Kemper, Alfons. 2023. “Technical Perspective: FoundationDB Performs Balancing Act.” Commun. ACM 66 (6): 96. doi.org/10.1145/3592837
  18. Khattab, Omar, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, et al. 2023. “DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines.” arXiv:2310.03714 [cs.CL]. arxiv.org/abs/2310.03714
  19. Kirillov, Alexander, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, et al. 2023. “Segment Anything.” arXiv:2304.02643 [cs.CV]. arxiv.org/abs/2304.02643
  20. Kuschewski, Maximilian, David Sauerwein, Adnan Alhomssi, and Viktor Leis. 2023. “BtrBlocks: Efficient Columnar Compression for Data Lakes.” Proceedings of the ACM on Management of Data 1 (2): 1–26.
  21. Pedreira, Pedro, Orri Erling, Konstantinos Karanasos, Scott Schneider, Wes McKinney, Satya R Valluri, Mohamed Zait, and Jacques Nadeau. 2023. “The Composable Data Management System Manifesto.” Proceedings of the VLDB Endowment 16 (10): 2679–2685. www.vldb.org/pvldb/vol16/p2679-pedreira.pdf
  22. Prince, Simon J.D. 2023. Understanding Deep Learning. The MIT Press. udlbook.com
  23. Qian, Chen, Xin Cong, Wei Liu, Cheng Yang, Weize Chen, Yusheng Su, Yufan Dang, et al. 2023. “Communicative Agents for Software Development.” arXiv:2307.07924 [cs.SE]. arxiv.org/abs/2307.07924
  24. Shi, Freda, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou. 2023. “Large Language Models Can Be Easily Distracted by Irrelevant Context.” arXiv:2302.00093 [cs.CL]. arxiv.org/abs/2302.00093
  25. Smith, David, Joseph Samuel Myers, Craig S. Kaplan, and Chaim Goodman-Strauss. 2023. “An Aperiodic Monotile.”
  26. Srivastava, Aarohi, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, et al. 2023. “Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models.” arXiv:2206.04615 [cs.CL]. arxiv.org/abs/2206.04615
  27. Touvron, Hugo, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, et al. 2023. “LLaMA: Open and Efficient Foundation Language Models.” arXiv:2302.13971 [cs.CL]. arxiv.org/abs/2302.13971
  28. Tu, Xinming, James Zou, Weijie J. Su, and Linjun Zhang. 2023. “What Should Data Science Education Do with Large Language Models?” arXiv:2307.02792 [cs.CY]. arxiv.org/abs/2307.02792
  29. Tuzhilin, Mikhail, and Dong Zhang. 2023. “Graphs, Algorithms and Applications.” arXiv:2312.11543 [cs.SI]. arxiv.org/abs/2312.11543
  30. Wu, Qingyun, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang (Eric) Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. “AutoGen: Enabling next-Gen LLM Applications via Multi-Agent Conversation.” Technical report MSR-TR-2023-33. Microsoft. www.microsoft.com/en-us/research/publication/autogen-enabling-next-gen-llm-applications-via-multi-agent-conversation-framework/
  31. Wu, Shijie, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023. “BloombergGPT: A Large Language Model for Finance.” arXiv:2303.17564 [cs.LG]. arxiv.org/abs/2303.17564
  32. Zhang, Yanzhe, Ruiyi Zhang, Jiuxiang Gu, Yufan Zhou, Nedim Lipka, Diyi Yang, and Tong Sun. 2023. “LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding.” arXiv:2306.17107 [cs.CV]. arxiv.org/abs/2306.17107
  33. Zhao, Yanli, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, et al. 2023. “PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.” arXiv:2304.11277 [cs.DC]. arxiv.org/abs/2304.11277

2022

  1. Roberts, Daniel A., Sho Yaida, and Boris Hanin. 2022. The Principles of Deep Learning Theory: An Effective Theory Approach to Understanding Neural Networks. Cambridge University Press. doi.org/10.1017/9781009023405
  2. Chaudhary, Himank. 2022. “Skipping the Boring Parts of Building a Database Using FoundationDB.” www.tigrisdata.com/blog/building-a-database-using-foundationdb/
  3. Chung, Hyung Won, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, et al. 2022. “Scaling Instruction-Finetuned Language Models.” arXiv:2210.11416 [cs.LG]. arxiv.org/abs/2210.11416
  4. Geiping, Jonas, and Tom Goldstein. 2022. “Cramming: Training a Language Model on a Single GPU in One Day.” arXiv:2212.14034 [cs.CL]. arxiv.org/abs/2212.14034
  5. Hao, Yaru, Yutao Sun, Li Dong, Zhixiong Han, Yuxian Gu, and Furu Wei. 2022. “Structured Prompting: Scaling in-Context Learning to 1,000 Examples.” arXiv:2212.06713 [cs.CL]. arxiv.org/abs/2212.06713
  6. Harizopoulos, Stavros, Taylor Hopper, Morton Mo, Shyam Sundar Chandrasekaran, Tongguang Chen, Yan Cui, Nandini Ganesh, Gary Helmling, Hieu Pham, and Sebastian Wong. 2022. “Meta's next-Generation Realtime Monitoring and Analytics Platform.” Proceedings of the VLDB Endowment 15 (12): 3522–3534. www.vldb.org/pvldb/vol15/p3522-mo.pdf
  7. Hoffmann, Jordan, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, et al. 2022. “Training Compute-Optimal Large Language Models.” arXiv:2203.15556 [cs.CL]. arxiv.org/abs/2203.15556
  8. Shankar, Shreya, Rolando Garcia, Joseph M. Hellerstein, and Aditya G. Parameswaran. 2022. “Operationalizing Machine Learning: An Interview Study.” arXiv:2209.09125 [cs.SE]. arxiv.org/abs/2209.09125
  9. Suzgun, Mirac, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, et al. 2022. “Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.” arXiv:2210.09261 [cs.CL]. arxiv.org/abs/2210.09261

2021

  1. Chen, Mark, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, et al. 2021. “Evaluating Large Language Models Trained on Code.” arxiv.org/abs/2107.03374
  2. Hendrycks, Dan, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. “Measuring Massive Multitask Language Understanding.” arXiv:2009.03300 [cs.CY]. arxiv.org/abs/2009.03300
  3. Hu, Edward J., Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. “LoRA: Low-rank Adaptation of Large Language Models.” arXiv:2106.09685 [cs.CL]. arxiv.org/abs/2106.09685