Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Azaria, Amos"'
Conversational tutoring systems (CTSs) offer learning experiences through interactions based on natural language. They are recognized for promoting cognitive engagement and improving learning outcomes, especially in reasoning tasks. Nonetheless, the
Externí odkaz:
http://arxiv.org/abs/2404.17460
Nowadays, the diffusion of information through social networks is a powerful phenomenon. One common way to model diffusions in social networks is the Independent Cascade (IC) model. Given a set of infected nodes according to the IC model, a natural p
Externí odkaz:
http://arxiv.org/abs/2401.11330
Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction. They are known to promote high levels of cognitive engagement and benefit learning outcomes, particularly in reasoning tasks. Nonetheless, the t
Externí odkaz:
http://arxiv.org/abs/2310.01420
Autor:
Safrai, Myriam, Azaria, Amos
As Large Language Models (LLMs) are predictive models building their response based on the words in the prompts, there is a risk that small talk and irrelevant information may alter the response and the suggestion given. Therefore, this study aims to
Externí odkaz:
http://arxiv.org/abs/2309.08625
This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains, including scientific writing, mathematics, education, programming, and healthcare. We explore the potential of ChatGPT to enhance productivity, streamli
Externí odkaz:
http://arxiv.org/abs/2306.03102
In many situations when people are assigned to coalitions, the utility of each person depends on the friends in her coalition. Additionally, in many situations, the size of each coalition should be bounded. This paper studies such coalition formation
Externí odkaz:
http://arxiv.org/abs/2306.01378
Autor:
Wu, Yue, Prabhumoye, Shrimai, Min, So Yeon, Bisk, Yonatan, Salakhutdinov, Ruslan, Azaria, Amos, Mitchell, Tom, Li, Yuanzhi
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexit
Externí odkaz:
http://arxiv.org/abs/2305.15486
Autor:
Wu, Yue, Min, So Yeon, Bisk, Yonatan, Salakhutdinov, Ruslan, Azaria, Amos, Li, Yuanzhi, Mitchell, Tom, Prabhumoye, Shrimai
Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging control tasks, either by action scoring, or action modeling (fine-tuning
Externí odkaz:
http://arxiv.org/abs/2305.02412
Autor:
Azaria, Amos, Mitchell, Tom
While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. In this paper, we provide evidence that the LLM's inter
Externí odkaz:
http://arxiv.org/abs/2304.13734
High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructured text documents, e.g., instruction manuals. Instruction manuals and
Externí odkaz:
http://arxiv.org/abs/2302.04449