State-of-the-art neural language models can now be used to solve ad-hoc language tasks
through zero-shot prompting without the need for supervised training. This approach has …

Neural language models are widely used; however, their model parameters often need to be
adapted to the specific domains and tasks of an application, which is time-and resource …

Recent growth in the popularity of large language models has led to their increased usage
for summarizing, predicting, and generating text, making it vital to help researchers and …

The human-centric explainable artificial intelligence (HCXAI) community has raised the
need for framing the explanation process as a conversation between human and machine …

Automatic side-by-side evaluation has emerged as a promising approach to evaluating the
quality of responses from large language models (LLMs). However, analyzing the results …

Artificial Intelligence (AI) systems are increasingly pervasive: Internet of Things, in-car
intelligent devices, robots, and virtual assistants, and their large-scale adoption makes it …

Evaluating large language models (LLMs) presents unique challenges. While automatic
side-by-side evaluation, also known as LLM-as-a-judge, has become a promising solution …

Human evaluation is increasingly critical for assessing large language models, capturing
linguistic nuances, and reflecting user preferences more accurately than traditional …

We present Sequence Salience, a visual tool for interactive prompt debugging with input
salience methods. Sequence Salience builds on widely used salience methods for text …

While transformer-based models have achieved state-of-the-art results in a variety of
classification and generation tasks, their black-box nature makes them challenging for …