Zobrazeno 1 - 10
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pro vyhledávání: '"Côté, Marc Alexandre"'
Large language models (LLMs) have been increasingly applied to tasks in language understanding and interactive decision-making, with their impressive performance largely attributed to the extensive domain knowledge embedded within them. However, the
Externí odkaz:
http://arxiv.org/abs/2407.17695
Autor:
Mohanty, Shrestha, Arabzadeh, Negar, Tupini, Andrea, Sun, Yuxuan, Skrynnik, Alexey, Zholus, Artem, Côté, Marc-Alexandre, Kiseleva, Julia
Seamless interaction between AI agents and humans using natural language remains a key goal in AI research. This paper addresses the challenges of developing interactive agents capable of understanding and executing grounded natural language instruct
Externí odkaz:
http://arxiv.org/abs/2407.08898
Autor:
Jansen, Peter, Côté, Marc-Alexandre, Khot, Tushar, Bransom, Erin, Mishra, Bhavana Dalvi, Majumder, Bodhisattwa Prasad, Tafjord, Oyvind, Clark, Peter
Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reasoning is challenging as running real-world experiments is often prohibit
Externí odkaz:
http://arxiv.org/abs/2406.06769
Autor:
Wang, Ruoyao, Todd, Graham, Xiao, Ziang, Yuan, Xingdi, Côté, Marc-Alexandre, Clark, Peter, Jansen, Peter
Virtual environments play a key role in benchmarking advances in complex planning and decision-making tasks but are expensive and complicated to build by hand. Can current language models themselves serve as world simulators, correctly predicting how
Externí odkaz:
http://arxiv.org/abs/2406.06485
While Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks, their substantial computational requirements and restricted number of calls constrain their practical utility, especially in long-horizon interac
Externí odkaz:
http://arxiv.org/abs/2405.02749
Embodied Instruction Following (EIF) is a crucial task in embodied learning, requiring agents to interact with their environment through egocentric observations to fulfill natural language instructions. Recent advancements have seen a surge in employ
Externí odkaz:
http://arxiv.org/abs/2403.03017
Autor:
Fu, Haotian, Sharma, Pratyusha, Stengel-Eskin, Elias, Konidaris, George, Roux, Nicolas Le, Côté, Marc-Alexandre, Yuan, Xingdi
We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework i
Externí odkaz:
http://arxiv.org/abs/2402.16354
We introduce Language Feedback Models (LFMs) that identify desirable behaviour - actions that help achieve tasks specified in the instruction - for imitation learning in instruction following. To train LFMs, we obtain feedback from Large Language Mod
Externí odkaz:
http://arxiv.org/abs/2402.07876
Autor:
Sordoni, Alessandro, Yuan, Xingdi, Côté, Marc-Alexandre, Pereira, Matheus, Trischler, Adam, Xiao, Ziang, Hosseini, Arian, Niedtner, Friederike, Roux, Nicolas Le
Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can be seen as stochastic language layers in a language network, where the learnable parameters are the natural lan
Externí odkaz:
http://arxiv.org/abs/2306.12509
In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks. We operationalize this as a task of generating text games, expressed as hun
Externí odkaz:
http://arxiv.org/abs/2305.14879