Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Bahdanau, Dzmitry"'
We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely through synthetic demonstrations. These demonstrations are collected by first interacting with a browser to generate trajectory rollouts, which are then retroactive
Externí odkaz:
http://arxiv.org/abs/2410.02907
In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-r
Externí odkaz:
http://arxiv.org/abs/2405.13022
Autor:
BehnamGhader, Parishad, Adlakha, Vaibhav, Mosbach, Marius, Bahdanau, Dzmitry, Chapados, Nicolas, Reddy, Siva
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is only slowly adopting these models for text embedding tasks, which require rich contextualized representations
Externí odkaz:
http://arxiv.org/abs/2404.05961
Contemporary Large Language Models (LLMs) exhibit a high degree of code generation and comprehension capability. A particularly promising area is their ability to interpret code modules from unfamiliar libraries for solving user-instructed tasks. Rec
Externí odkaz:
http://arxiv.org/abs/2311.09635
Data augmentation is a widely used technique to address the problem of text classification when there is a limited amount of training data. Recent work often tackles this problem using large language models (LLMs) like GPT3 that can generate new exam
Externí odkaz:
http://arxiv.org/abs/2310.14192
Humans possess a remarkable ability to assign novel interpretations to linguistic expressions, enabling them to learn new words and understand community-specific connotations. However, Large Language Models (LLMs) have a knowledge cutoff and are cost
Externí odkaz:
http://arxiv.org/abs/2310.11634
In-context learning (ICL) using large language models for tasks with many labels is challenging due to the limited context window, which makes it difficult to fit a sufficient number of examples in the prompt. In this paper, we use a pre-trained dens
Externí odkaz:
http://arxiv.org/abs/2309.10954
Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g., imports, parent classes, files with similar names, etc.), thereby prod
Externí odkaz:
http://arxiv.org/abs/2306.10998
Autor:
Li, Raymond, Allal, Loubna Ben, Zi, Yangtian, Muennighoff, Niklas, Kocetkov, Denis, Mou, Chenghao, Marone, Marc, Akiki, Christopher, Li, Jia, Chim, Jenny, Liu, Qian, Zheltonozhskii, Evgenii, Zhuo, Terry Yue, Wang, Thomas, Dehaene, Olivier, Davaadorj, Mishig, Lamy-Poirier, Joel, Monteiro, João, Shliazhko, Oleh, Gontier, Nicolas, Meade, Nicholas, Zebaze, Armel, Yee, Ming-Ho, Umapathi, Logesh Kumar, Zhu, Jian, Lipkin, Benjamin, Oblokulov, Muhtasham, Wang, Zhiruo, Murthy, Rudra, Stillerman, Jason, Patel, Siva Sankalp, Abulkhanov, Dmitry, Zocca, Marco, Dey, Manan, Zhang, Zhihan, Fahmy, Nour, Bhattacharyya, Urvashi, Yu, Wenhao, Singh, Swayam, Luccioni, Sasha, Villegas, Paulo, Kunakov, Maxim, Zhdanov, Fedor, Romero, Manuel, Lee, Tony, Timor, Nadav, Ding, Jennifer, Schlesinger, Claire, Schoelkopf, Hailey, Ebert, Jan, Dao, Tri, Mishra, Mayank, Gu, Alex, Robinson, Jennifer, Anderson, Carolyn Jane, Dolan-Gavitt, Brendan, Contractor, Danish, Reddy, Siva, Fried, Daniel, Bahdanau, Dzmitry, Jernite, Yacine, Ferrandis, Carlos Muñoz, Hughes, Sean, Wolf, Thomas, Guha, Arjun, von Werra, Leandro, de Vries, Harm
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilitie
Externí odkaz:
http://arxiv.org/abs/2305.06161
Autor:
Allal, Loubna Ben, Li, Raymond, Kocetkov, Denis, Mou, Chenghao, Akiki, Christopher, Ferrandis, Carlos Munoz, Muennighoff, Niklas, Mishra, Mayank, Gu, Alex, Dey, Manan, Umapathi, Logesh Kumar, Anderson, Carolyn Jane, Zi, Yangtian, Poirier, Joel Lamy, Schoelkopf, Hailey, Troshin, Sergey, Abulkhanov, Dmitry, Romero, Manuel, Lappert, Michael, De Toni, Francesco, del Río, Bernardo García, Liu, Qian, Bose, Shamik, Bhattacharyya, Urvashi, Zhuo, Terry Yue, Yu, Ian, Villegas, Paulo, Zocca, Marco, Mangrulkar, Sourab, Lansky, David, Nguyen, Huu, Contractor, Danish, Villa, Luis, Li, Jia, Bahdanau, Dzmitry, Jernite, Yacine, Hughes, Sean, Fried, Daniel, Guha, Arjun, de Vries, Harm, von Werra, Leandro
The BigCode project is an open-scientific collaboration working on the responsible development of large language models for code. This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Per
Externí odkaz:
http://arxiv.org/abs/2301.03988