Large language models like GPT-4 exhibit emergent capabilities across general-purpose
tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks …

Large language models exhibit surprising emergent generalization properties, yet also
struggle on many simple reasoning tasks such as arithmetic and parity. This raises the …

Large language models (LLMs) have shown increasing in-context learning capabilities
through scaling up model and data size. Despite this progress, LLMs are still unable to solve …

Creating learning models that can exhibit sophisticated reasoning abilities is one of the
greatest challenges in deep learning research, and mathematics is rapidly becoming one of …

We introduce Goat, a fine-tuned LLaMA model that significantly outperforms GPT-4 on a
range of arithmetic tasks. Fine-tuned on a synthetically generated dataset, Goat achieves …

Neural metrics have achieved impressive correlation with human judgements in the
evaluation of machine translation systems, but before we can safely optimise towards such …

Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools
like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation …

Numbers are crucial for various real-world domains such as finance, economics, and
science. Thus, understanding and reasoning with numbers are essential skills for language …

The ability to extrapolate, ie, to make predictions on sequences that are longer than those
presented as training examples, is a challenging problem for current deep learning models …

Background Large Language Models (LLMs) like GPT-3.5 and GPT-4 are increasingly
entering the healthcare domain as a proposed means to assist with administrative tasks. To …