As Large Language Models (LLMs) become popular, there emerged an important trend of
using multimodality to augment the LLMs' generation ability, which enables LLMs to better …

Abstract The Abstraction and Reasoning Corpus (ARC)(Chollet, 2019) and its most recent
language-complete instantiation (LARC) has been postulated as an important step towards …

Text-based reinforcement learning agents have predominantly been neural network-based
models with embeddings-based representation, learning uninterpretable policies that often …

Text-based games (TBG) have emerged as promising environments for driving research in
grounded language understanding and studying problems like generalization and sample …

Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring
reinforcement learning (RL) agents to combine natural language understanding with …

Entity linking methods based on dense retrieval are an efficient and widely used solution in
large-scale applications, but they fall short of the performance of generative models, as they …

Text-based games offer a challenging test bed to evaluate virtual agents at language
understanding, multi-step problem-solving, and common-sense reasoning. However, speed …

Interactive fiction games have emerged as an important vehicle to improve the
generalization and compositional reasoning capabilities of language-based reinforcement …

World models have improved the ability of reinforcement learning agents to operate in a
sample efficient manner, by being trained to predict plausible changes in the underlying …

The ability to reason, plan and solve highly abstract problems is a hallmark of human
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …