Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Artificial neural networks (ANNs) have emerged as an essential tool in machine learning,
achieving remarkable success across diverse domains, including image and speech …

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 …

Agents capable of carrying out general tasks on a computer can improve efficiency and
productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally …

We study how in-context learning (ICL) in language models is affected by semantic priors
versus input-label mappings. We investigate two setups-ICL with flipped labels and ICL with …

Several recent works demonstrate that transformers can implement algorithms like gradient
descent. By a careful construction of weights, these works show that multiple layers of …

Neural sequence models based on the transformer architecture have demonstrated
remarkable\emph {in-context learning}(ICL) abilities, where they can perform new tasks …

Large Language Models (LLMs) have shown remarkable success in various tasks, yet their
safety and the risk of generating harmful content remain pressing concerns. In this paper, we …

Large pretrained language models have shown surprising in-context learning (ICL) ability.
With a few demonstration input-label pairs, they can predict the label for an unseen input …