Deep neural-network-based language models (LMs) are increasingly applied to large-scale
protein sequence data to predict protein function. However, being largely black-box models …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

With the increasing capabilities of large language models (LLMs), in-context learning (ICL)
has emerged as a new paradigm for natural language processing (NLP), where LLMs make …

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 …

The Internet contains a wealth of knowledge—from the birthdays of historical figures to
tutorials on how to code—all of which may be learned by language models. However, while …

Large transformer models trained on diverse datasets have shown a remarkable ability to
learn in-context, achieving high few-shot performance on tasks they were not explicitly …

Large language models can perform various reasoning tasks by using chain-of-thought
prompting, which guides them to find answers through step-by-step demonstrations …

Large language models based on transformers have achieved great empirical successes.
However, as they are deployed more widely, there is a growing need to better understand …

Understanding in-context learning (ICL) capability that enables large language models
(LLMs) to excel in proficiency through demonstration examples is of utmost importance. This …

Large language models (LLMs) demonstrate emergent in-context learning capabilities,
where they adapt to new tasks based on example demonstrations. However, in-context …