References

References#

Work in Progress (TODO: move elsewhere)

important and/or related to whole book

unclassified

Couldn’t decide which chapter(s) these links are related to. They’re mostly about security & optimisation. Perhaps create a new chapter?

[1]

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Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, and others. Evaluating large language models trained on code. 2021. arXiv:2107.03374.

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Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, and others. Beyond the imitation game: quantifying and extrapolating the capabilities of language models. 2023. arXiv:2206.04615.

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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman. GLUE: a multi-task benchmark and analysis platform for natural language understanding. 2019. arXiv:1804.07461.

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Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. The LAMBADA dataset: word prediction requiring a broad discourse context. 2016. arXiv:1606.06031.

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Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. AlpacaFarm: a simulation framework for methods that learn from human feedback. 2023. arXiv:2305.14387.

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Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers. MTEB: massive text embedding benchmark. 2023. arXiv:2210.07316.

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Chip Huyen. Building LLM applications for production. 2023. URL: https://huyenchip.com/2023/04/11/llm-engineering.html.

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Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, and others. GPT-NeoX 20B: an open-source autoregressive language model. 2022. arXiv:2204.06745.

[94]

Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, and others. OPT: open pre-trained transformer language models. 2022. arXiv:2205.01068.

[95]

Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. RoFormer: enhanced transformer with rotary position embedding. 2022. arXiv:2104.09864.

[96]

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[97]

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[100]

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[101]

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[102]

Biao Zhang and Rico Sennrich. Root mean square layer normalisation. 2019. arXiv:1910.07467.

[103]

Noam Shazeer. GLU variants improve transformer. 2020. arXiv:2002.05202.

[104]

Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. Self-instruct: aligning language models with self-generated instructions. 2023. arXiv:2212.10560.

[105]

Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. Training deep nets with sublinear memory cost. 2016. arXiv:1604.06174.

[106]

Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. FlashAttention: fast and memory-efficient exact attention with IO-awareness. 2022. arXiv:2205.14135.

[107]

Renrui Zhang, Jiaming Han, Chris Liu, Peng Gao, Aojun Zhou, Xiangfei Hu, Shilin Yan, Pan Lu, and others. LLaMA-Adapter: efficient fine-tuning of language models with zero-init attention. 2023. arXiv:2303.16199.

[108]

Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, and others. OpenAssistant conversations – democratizing large language model alignment. 2023. arXiv:2304.07327.

[109]

Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, and Daxin Jiang. WizardLM: empowering large language models to follow complex instructions. 2023. arXiv:2304.12244.

[110]

Ofir Press, Noah A. Smith, and Mike Lewis. Train short, test long: attention with linear biases enables input length extrapolation. 2022. arXiv:2108.12409.

[111]

Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, and Julien Launay. The refinedweb dataset for falcon LLM: outperforming curated corpora with web data, and web data only. 2023. arXiv:2306.01116.

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Noam Shazeer. Fast transformer decoding: one write-head is all you need. 2019. arXiv:1911.02150.

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Shashank Sonkar and Richard G. Baraniuk. Investigating the role of feed-forward networks in transformers using parallel attention and feed-forward net design. 2023. arXiv:2305.13297.

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Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, and others. LLaMA 2: open foundation and fine-tuned chat models. 2023. arXiv:2307.09288.

[115]

Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. GQA: training generalised multi-query transformer models from multi-head checkpoints. 2023. arXiv:2305.13245.

[116]

Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, and others. PaLM: scaling language modeling with pathways. 2022. arXiv:2204.02311.

[117]

Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. SDXL: improving latent diffusion models for high-resolution image synthesis. 2023. arXiv:2307.01952.

[118]

Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, and others. WizardCoder: empowering code large language models with evol-instruct. 2023. arXiv:2306.08568.

[119]

Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, and others. Code LLaMA: open foundation models for code. 2023. arXiv:2308.12950.

[120]

Alex Henry, Prudhvi Raj Dachapally, Shubham Pawar, and Yuxuan Chen. Query-key normalization for transformers. 2020. arXiv:2010.04245.

[121]

Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, and others. Scaling vision transformers to 22 billion parameters. 2023. arXiv:2302.05442.

[122]

Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, and others. Flamingo: a visual language model for few-shot learning. 2022. arXiv:2204.14198.

[123]

Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. Generating long sequences with sparse transformers. 2019. arXiv:1904.10509.

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Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer: the long-document transformer. 2020. arXiv:2004.05150.

[125]

Will Knight. The myth of open source AI. 2023. URL: https://www.wired.com/story/the-myth-of-open-source-ai.

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The reversal curse: LLMs trained on "A is B" fail to learn "B is A". 2023. URL: https://twitter.com/OwainEvans_UK/status/1705285631520407821.

[127]

Eric Hartford. Uncensored models. 2023. URL: https://erichartford.com/uncensored-models.

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Zac Amos. What is FraudGPT? 2023. URL: https://hackernoon.com/what-is-fraudgpt.

[129]

Rakesh Krishnan. FraudGPT: the villain avatar of ChatGPT. 2023. URL: https://netenrich.com/blog/fraudgpt-the-villain-avatar-of-chatgpt.

[130]

Daniel Kelley. WormGPT – the generative AI tool cybercriminals are using to launch business email compromise attacks. 2023. URL: https://slashnext.com/blog/wormgpt-the-generative-ai-tool-cybercriminals-are-using-to-launch-business-email-compromise-attacks.

[131]

Mandy. What is PoisonGPT and how does it work? 2023. URL: https://aitoolmall.com/news/what-is-poisongpt.

[132]

Daniel Huynh and Jade Hardouin. PoisonGPT: how we hid a lobotomised LLM on Hugging Face to spread fake news. 2023. URL: https://blog.mithrilsecurity.io/poisongpt-how-we-hid-a-lobotomized-llm-on-hugging-face-to-spread-fake-news.

[133]

Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar. ToxiGen: a large-scale machine-generated dataset for adversarial and implicit hate speech detection. 2022. arXiv:2203.09509.

[134]

Roger Montti. New open source LLM with zero guardrails rivals google's PaLM 2. 2023. URL: https://www.searchenginejournal.com/new-open-source-llm-with-zero-guardrails-rivals-google-palm-2/496212.

[135]

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