As language models grow in popularity, it becomes increasingly important to clearly
measure all possible markers of demographic identity in order to avoid perpetuating existing …

Technology companies continue to invest in efforts to incorporate responsibility in their
Artificial Intelligence (AI) advancements, while efforts to audit and regulate AI systems …

To enable building and testing models on long-document comprehension, we introduce
QuALITY, a multiple-choice QA dataset with context passages in English that have an …

A recurring challenge of crowdsourcing NLP datasets at scale is that human writers often
rely on repetitive patterns when crafting examples, leading to a lack of linguistic diversity. We …

Abstract Scaling Visual Question Answering (VQA) to the open-domain and multi-hop nature
of web searches, requires fundamental advances in visual representation learning …

In recent years, progress in NLU has been driven by benchmarks. These benchmarks are
typically collected by crowdsourcing, where annotators write examples based on annotation …

Most benchmark datasets targeting commonsense reasoning focus on everyday scenarios:
physical knowledge like knowing that you could fill a cup under a waterfall [Talmor et al …

This PhD project leverages advancements in multimodal large language models to build an
inclusive collaboration feedback loop, in order to facilitate the automated detection …

To create models that are robust across a wide range of test inputs, training datasets should
include diverse examples that span numerous phenomena. Dynamic adversarial data …

Prompting language models to provide step-by-step answers (eg," Chain-of-Thought") is the
prominent approach for complex reasoning tasks, where more accurate reasoning chains …