Генеративный искусственный интеллект. Как ИИ меняет нашу жизнь и работу: читать книгу онлайн полностью и бесплатно

53. «Introducing LLaMA: A foundational, 65‐billion-parameter language model». https://ai.facebook.com/blog/large-language-model-llama-meta-ai/.

54. «LLaMA: Open and Efficient Foundation Language Models». https://arxiv.org/pdf/2302.13971.pdf.

55. A. Hern, «TechScape: Will Meta’s * massive leak democratise AI – and at what cost?». The Guardian, The Guardian, Mar. 07, 2023. (Онлайн). https://www.theguardian.com/technology/2023/mar/07/techscape-meta-leak-llama-chatgpt-ai-crossroads.

56. Facebook* company, «Meta [128] and Microsoft Introduce the Next Generation of Llama». Meta *, Jul. 18, 2023. https://about.fb.com/news/2023/07/llama‐2/.

2. Обучение больших языковых моделей

1. J. Wei et al., «Emergent Abilities of Large Language Models». Jun. 15, 2022. (Онлайн). http://arxiv.org/abs/2206.07682.

2. R. Schaeffer, B. Miranda, and S. Koyejo, «Are Emergent Abilities of Large Language Models a Mirage?». Apr. 28, 2023. (Онлайн). http://arxiv.org/abs/2304.15004.

3. «Language Models are Few-Shot Learners». https://arxiv.org/pdf/2005.14165.pdf.

4. «eDiscovery Best Practices: Perspective on the Amount of Data Contained in 1 Gigabyte». CloudNine, Mar. 05, 2012. https://cloudnine.com/ediscoverydaily/electronic-discovery/ediscovery-best-practices-perspective-on-the-amount-of-data-contained-in‐1‐gigabyte/.

5. «On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?». ACM Digital Library. https://dl.acm.org/doi/pdf/10.1145/ 3442188.3445922.

6. A. Caliskan, J. J. Bryson, and A. Narayanan, «Semantics derived automatically from language corpora contain human-like biases». Science, vol. 356, no. 6334, pp. 183–186, Apr. 2017, doi: https://doi.org/10.1126/science.aal4230.

7. «Persistent Anti-Muslim Bias in Large Language Models». https://arxiv.org/pdf/2101.05783.pdf.

8. «Gender and Representation Bias in GPT‐3 Generated Stories». https://aclanthology.org/2021.nuse‐1.5.pdf.

9. «StereoSet: Measuring stereotypical bias in pretrained language models». https://aclanthology.org/2021.acl-long.416.pdf.

10. «Black Lives Matter in Wikipedia: Collaboration and Collective Memory around Online Social Movements». https://dl.acm.org/doi/pdf/10.1145/2998181.2998232.

11. «Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings». https://arxiv.org/pdf/1607.06520.pdf.

12. «An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models». https://arxiv.org/pdf/2110.08527.pdf.

13. «Hugging Face Dataset Cards». https://huggingface.co/docs/hub/datasets-cards.

14. «The ROOTS Search Tool: Data Transparency for LLMs». https://arxiv.org/pdf/2302.14035.pdf.

15. «Extracting Training Data from Large Language Models». https://arxiv.org/pdf/2012.07805.pdf.

16. «The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks». https://arxiv.org/pdf/1802.08232.pdf.

17. «Protecting privacy in practice: The current use, development and limits of Privacy Enhancing Technologies in data analysis». https://royalsociety.org/-/media/policy/projects/privacy-enhancing-technologies/Protecting-privacy-in-practice.pdf.

18. E. M. Renieris, Beyond Data: Reclaiming Human Rights at the Dawn of the Metaverse. MIT Press, 2023. (Онлайн). https://books.google.com/books/about/Beyond_Data.html?hl=&id=zJZuEAAAQBAJ.

3. Конфиденциальность и безопасность данных в аспекте LLM

1. «OpenAI Chatbot Spits Out Biased Musings, Despite Guardrails». Bloomberg. https://www.bloomberg.com/news/newsletters/2022-12-08/chatgpt-open-ai-s-chatbot-is-spitting-out-biased-sexist-results.

2. A. Askell et al., «A General Language Assistant as a Laboratory for Alignment». Dec. 01, 2021. (Онлайн). http://arxiv.org/abs/2112.00861.

3. H. Ngo et al., «Mitigating harm in language models with conditional-likelihood filtration». Aug. 04, 2021. (Онлайн). http://arxiv.org/abs/2108.07790.

4. T. Korbak et al., «Pretraining Language Models with Human Preferences». Feb. 16, 2023. (Онлайн). http://arxiv.org/abs/2302.08582.

5. P. Christiano, J. Leike, T. B. Brown, M. Martic, S. Legg, and D. Amodei, «Deep reinforcement learning from human preferences». Jun. 12, 2017. (Онлайн). http://arxiv.org/abs/1706.03741.

6. B. Perrigo, «Exclusive: OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic». Time, Jan. 18, 2023. https://time.com/6247678/openai-chatgpt-kenya-workers.

[128] Организация признана экстремистской и запрещена на территории РФ.