Generative AI (GenAI) represents a shift from AI's ability to “recognize” to its ability to
“generate” solutions for a wide range of tasks. As generated solutions and applications grow …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language processing. However, their internal mechanisms are still unclear and this lack of …

Interpretable machine learning has exploded as an area of interest over the last decade,
sparked by the rise of increasingly large datasets and deep neural networks …

Although the applications of artificial intelligence especially deep learning have greatly
improved various aspects of intelligent manufacturing, they still face challenges for broader …

Natural language is an appealing medium for explaining how large language models
process and store information, but evaluating the faithfulness of such explanations is …

In this survey, we address the key challenges in Large Language Models (LLM) research,
focusing on the importance of interpretability. Driven by increasing interest from AI and …

Extracting patient information from unstructured text is a critical task in health decision-
support and clinical research. Large language models (LLMs) have shown the potential to …

In this paper, we describe the capabilities and constraints of Large Language Models
(LLMs) within disparate academic disciplines, aiming to delineate their strengths and …

In human-written articles, we often leverage the subtleties of text style, such as bold and
italics, to guide the attention of readers. These textual emphases are vital for the readers to …

Large Language Models (LLMs) have achieved remarkable success, demonstrating
powerful instruction-following capabilities across diverse tasks. Instruction fine-tuning is …