Currently, there is little agreement as to how Natural Language Generation (NLG) systems
should be evaluated, with a particularly high degree of variation in the way that human …

Large language model (LLM) agents have been shown effective on a wide range of tasks,
and by ensembling multiple LLM agents, their performances could be further improved …

Through multi-agent competition, the simple objective of hide-and-seek, and standard
reinforcement learning algorithms at scale, we find that agents create a self-supervised …

Common methods for aligning large language models (LLMs) with desired behaviour
heavily rely on human-labelled data. However, as models grow increasingly sophisticated …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

Measuring the human sense of place and quantifying the connections among the visual
features of the built environment that impact the human sense of place have long been of …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

This paper presents the results of the WMT17 shared tasks, which included three machine
translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and …