The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

The scaling of Transformers has driven breakthrough capabilities for language models. At
present, the largest large language models (LLMs) contain upwards of 100B parameters …

Recent developments in large language models (LLMs) have been impressive. However,
these models sometimes show inconsistencies and problematic behavior, such as …

Empowering large language models to accurately express confidence in their answers is
essential for trustworthy decision-making. Previous confidence elicitation methods, which …

We study whether language models can evaluate the validity of their own claims and predict
which questions they will be able to answer correctly. We first show that larger models are …

We show that a GPT-3 model can learn to express uncertainty about its own answers in
natural language--without use of model logits. When given a question, the model generates …

Neural networks have achieved remarkable performance across various problem domains,
but their widespread applicability is hindered by inherent limitations such as overconfidence …

The proliferation of open-source Large Language Models (LLMs) from various institutions
has highlighted the urgent need for comprehensive evaluation methods. However, current …

The increased deployment of LMs for real-world tasks involving knowledge and facts makes
it important to understand model epistemology: what LMs think they know, and how their …

Localizing objects in image collections without supervision can help to avoid expensive
annotation campaigns. We propose a simple approach to this problem, that leverages the …