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LLM

This module explores Large Language Models (LLMs), focusing on their development and applications. It covers foundational concepts in natural language processing, the architecture of models like GPT (Generative Pre-trained Transformer), and their applications in tasks like text generation, translation, and content creation, along with ethical considerations and potential biases.

Portale > Intelligenza artificiale > LLM

Costruttore di programmi di studio

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Li, Yujia, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, et al. “Competition-Level Code Generation with AlphaCode.” Science 378, no. 6624 (December 9, 2022): 1092–97.

https://doi.org/10.1126/science.abq1158

Stiennon, Nisan, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. “Learning to Summarize from Human Feedback.” arXiv, February 15, 2022.

http://arxiv.org/abs/2009.01325

Garg, Shivam, Dimitris Tsipras, Percy Liang, and Gregory Valiant. “What Can Transformers Learn In-Context? A Case Study of Simple Function Classes.” arXiv, August 11, 2023.

http://arxiv.org/abs/2208.01066

He, Junxian, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. “Towards a Unified View of Parameter-Efficient Transfer Learning.” arXiv, February 2, 2022.

http://arxiv.org/abs/2110.04366

Zhang, Susan, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, et al. “OPT: Open Pre-Trained Transformer Language Models.” arXiv, 2022.

https://doi.org/10.48550/ARXIV.2205.01068

Clark, Kevin, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. “ELECTRA: Pre-Training Text Encoders as Discriminators Rather Than Generators.” arXiv, 2020.

https://doi.org/10.48550/ARXIV.2003.10555

Liu, Yinhan, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. “RoBERTa: A Robustly Optimized BERT Pretraining Approach.” arXiv, July 26, 2019.

http://arxiv.org/abs/1907.11692

Radford, Alec, and Karthik Narasimhan. “Improving Language Understanding by Generative Pre-Training,” 2018.

https://www.semanticscholar.org/paper/Improving-Language-Understanding-by-Generative-Radford-Narasimhan/cd18800a0fe0b668a1cc19f2ec95b5003d0a5035

Peters, Matthew E., Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. “Deep Contextualized Word Representations.” arXiv, 2018.

https://doi.org/10.48550/ARXIV.1802.05365

Manning, Christopher D. “Human Language Understanding & Reasoning.” Daedalus 151, no. 2 (May 1, 2022): 127–38.

https://doi.org/10.1162/daed_a_01905

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Apply for: Li, Yujia, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, et al. “Competition-Level Code Generation with AlphaCode.” Science 378, no. 6624 (December 9, 2022): 1092–97.

Li, Yujia, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, et al. “Competition-Level Code Generation with AlphaCode.” Science 378, no. 6624 (December 9, 2022): 1092–97.

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