While counterfactual examples are useful for analysis and training of NLP models, current
generation methods either rely on manual labor to create very few counterfactuals, or only …

Over the years, the task of AI-assisted data annotation has seen remarkable advancements.
However, a specific type of annotation task, the qualitative coding performed during thematic …

We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that
covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as …

Abstract Named Entity Recognition (NER) remains difficult in real-world settings; current
challenges include short texts (low context), emerging entities, and complex entities (eg …

The in-context learning capabilities of LLMs like GPT-3 allow annotators to customize an
LLM to their specific tasks with a small number of examples. However, users tend to include …

Large language models (LLMs) are capable of generating multiple responses to a single
prompt, yet little effort has been expended to help end-users or system designers make use …

Interpretability methods aim to help users build trust in and understand the capabilities of
machine learning models. However, existing approaches often rely on abstract, complex …

Benchmark datasets play an important role in evaluating Natural Language Understanding
(NLU) models. However, shortcuts—unwanted biases in the benchmark datasets—can …

Large Language Models (LLMs) have gained significant attention but also raised concerns
due to the risk of misuse. Jailbreak prompts, a popular type of adversarial attack towards …

Data augmentation is crucial to make machine learning models more robust and safe.
However, augmenting data can be challenging as it requires generating diverse data points …