AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …

This survey article proposes a clearer view of Natural Language Reasoning (NLR) in the
field of Natural Language Processing (NLP), both conceptually and practically …

Large language models have achieved impressive performance on various natural
language processing tasks. However, so far they have been evaluated primarily on …

The current trend of scaling language models involves increasing both parameter count and
training dataset size. Extrapolating this trend suggests that training dataset size may soon be …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

Language models show a surprising range of capabilities, but the source of their apparent
competence is unclear. Do these networks just memorize a collection of surface statistics, or …

While large language models (LMs) have shown remarkable capabilities across numerous
tasks, they often struggle with simple reasoning and planning in physical environments …

Large language models (LLMs) have been shown to be capable of impressive few-shot
generalisation to new tasks. However, they still tend to perform poorly on multi-step logical …

This paper explores the impact of extending input lengths on the capabilities of Large
Language Models (LLMs). Despite LLMs advancements in recent times, their performance …

Does prompting a large language model (LLM) like GPT-3 with explanations improve in-
context learning? We study this question on two NLP tasks that involve reasoning over text …