Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

Language models are increasingly attracting interest from writers. However, such models
lack long-range semantic coherence, limiting their usefulness for longform creative writing …

We address the general task of structured commonsense reasoning: given a natural
language input, the goal is to generate a graph such as an event--or a reasoning-graph. To …

In this work, we introduce SMART-LLM, an innovative framework designed for embodied
multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task Planning using Large …

Recent advancements in Large Language Models (LLMs) showcase advanced reasoning,
yet NLP evaluations often depend on static benchmarks. Evaluating this necessitates …

This survey summarises the most recent methods for building and assessing helpful, honest,
and harmless neural language models, considering small, medium, and large-size models …

In everyday life, humans often plan their actions by following step-by-step instructions in the
form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for …

Event schemas are a form of world knowledge about the typical progression of events.
Recent methods for event schema induction use information extraction systems to construct …

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 …

Commonsense capabilities of pre-trained language models dramatically improve with scale,
leading many to believe that scale is the only winning recipe. But is it? Here, we investigate …