3 accepted papers
Automated interpretability aims to translate large language model (LLM) features into human understandable descriptions. However, natural language feature descriptions are often vague, inconsistent, and require manual relabeling. In response, we introduce *semantic regexes*, structured language desc…
Captions that describe or explain charts help improve recall and comprehension of the depicted data and provide a more accessible medium for people with visual disabilities. However, current approaches for automatically generating such captions struggle to articulate the perceptual or cognitive feat…
Expert decision makers are starting to rely on data-driven automated agents to assist them with various tasks. For this collaboration to perform properly, the human decision maker must have a mental model of when and when not to rely on the agent. In this work, we aim to ensure that human decision m…