The increasing reliance on Large Language Models (LLMs) across academia and industry
necessitates a comprehensive understanding of their robustness to prompts. In response to …

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …

Pretrained Language Models (LMs) have demonstrated ability to perform numerical
reasoning by extrapolating from a few examples in few-shot settings. However, the extent to …

The impressive performance of recent language models across a wide range of tasks
suggests that they possess a degree of abstract reasoning skills. Are these skills general …

Although large language models (LLMs) are widely deployed, the data used to train them is
rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but …

In this work, we carry out a data archaeology to infer books that are known to ChatGPT and
GPT-4 using a name cloze membership inference query. We find that OpenAI models have …

Large language models (LLMs) offer impressive performance in various zero-shot and few-
shot tasks. However, their success in zero-shot or few-shot settings may be affected by task …

With the rise of Large Language Models (LLMs) in recent years, new opportunities are
emerging, but also new challenges, and contamination is quickly becoming critical …

Text classification in natural language processing (NLP) is evolving rapidly, particularly with
the surge in transformer-based models, including large language models (LLM). This paper …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …