Zobrazeno 1 - 10
of 586
pro vyhledávání: '"P. Malfa"'
Fairness in Multi-Agent Systems (MAS) has been extensively studied, particularly in reward distribution among agents in scenarios such as goods allocation, resource division, lotteries, and bargaining systems. Fairness in MAS depends on various facto
Externí odkaz:
http://arxiv.org/abs/2410.12889
Autor:
Lin, Fangru, Mao, Shaoguang, La Malfa, Emanuele, Hofmann, Valentin, de Wynter, Adrian, Yao, Jing, Chen, Si-Qing, Wooldridge, Michael, Wei, Furu
Language is not monolithic. While many benchmarks are used as proxies to systematically estimate Large Language Models' (LLM) performance in real-life tasks, they tend to ignore the nuances of within-language variation and thus fail to model the expe
Externí odkaz:
http://arxiv.org/abs/2410.11005
Autor:
Marro, Samuele, La Malfa, Emanuele, Wright, Jesse, Li, Guohao, Shadbolt, Nigel, Wooldridge, Michael, Torr, Philip
Communication is a prerequisite for collaboration. When scaling networks of AI-powered agents, communication must be versatile, efficient, and portable. These requisites, which we refer to as the Agent Communication Trilemma, are hard to achieve in l
Externí odkaz:
http://arxiv.org/abs/2410.11905
The proposed two-dimensional geometrically exact beam element extends our previous work by including the effects of shear distortion, and also of distributed forces and moments acting along the beam. The general flexibility-based formulation exploits
Externí odkaz:
http://arxiv.org/abs/2410.04915
Deep Neural Networks (DNNs) can be represented as graphs whose links and vertices iteratively process data and solve tasks sub-optimally. Complex Network Theory (CNT), merging statistical physics with graph theory, provides a method for interpreting
Externí odkaz:
http://arxiv.org/abs/2404.11172
Autor:
Huang, X. Angelo, La Malfa, Emanuele, Marro, Samuele, Asperti, Andrea, Cohn, Anthony, Wooldridge, Michael
Theory of Mind (ToM) can be used to assess the capabilities of Large Language Models (LLMs) in complex scenarios where social reasoning is required. While the research community has proposed many ToM benchmarks, their hardness varies greatly, and the
Externí odkaz:
http://arxiv.org/abs/2406.11911
Autor:
Lin, Fangru, La Malfa, Emanuele, Hofmann, Valentin, Yang, Elle Michelle, Cohn, Anthony, Pierrehumbert, Janet B.
Planning is a fundamental property of human intelligence. Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time costs. Can large language models (LLMs) succeed at this task? Here, we pre
Externí odkaz:
http://arxiv.org/abs/2402.02805
Autor:
La Malfa, Emanuele, Weinhuber, Christoph, Torre, Orazio, Lin, Fangru, Marro, Samuele, Cohn, Anthony, Shadbolt, Nigel, Wooldridge, Michael
Many reasoning, planning, and problem-solving tasks share an intrinsic algorithmic nature: correctly simulating each step is a sufficient condition to solve them correctly. This work studies to what extent Large Language Models (LLMs) can simulate co
Externí odkaz:
http://arxiv.org/abs/2401.09074
Autor:
La Malfa, Emanuele, Petrov, Aleksandar, Frieder, Simon, Weinhuber, Christoph, Burnell, Ryan, Cohn, Anthony G., Shadbolt, Nigel, Wooldridge, Michael
Some of the most powerful language models currently are proprietary systems, accessible only via (typically restrictive) web or software programming interfaces. This is the Language-Models-as-a-Service (LMaaS) paradigm. Contrasting with scenarios whe
Externí odkaz:
http://arxiv.org/abs/2309.16573
Recent language models have shown impressive multilingual performance, even when not explicitly trained for it. Despite this, there are concerns about the quality of their outputs across different languages. In this paper, we show how disparity in th
Externí odkaz:
http://arxiv.org/abs/2305.15425