Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Advancements in artificial intelligence are rapidly increasing. The new‐generation large
language models, such as ChatGPT and GPT‐4, bear the potential to transform educational …

The recent emergence and adoption of Machine Learning technology, and specifically of
Large Language Models, has drawn attention to the need for systematic and transparent …

NLP research is impeded by a lack of resources and awareness of the challenges presented
by underrepresented languages and dialects. Focusing on the languages spoken in …

Creating high-quality annotated data for task-oriented dialog (ToD) is known to be
notoriously difficult, and the challenges are amplified when the goal is to create equitable …

The importance of meeting data quality standards in the context of Machine Learning (ML)
development pipelines is explored in this study. It delves deep into why good data is crucial …

Text data can pose a risk of harm. However, the risks are not fully understood, and how to
handle, present, and discuss harmful text in a safe way remains an unresolved issue in the …

The fitness of the systems in which Machine Learning (ML) is used depends greatly on good-
quality data. Specifications on what makes a good-quality dataset have traditionally been …

Machine learning (ML) methods are proliferating in scientific research. However, the
adoption of these methods has been accompanied by failures of validity, reproducibility, and …

Historically, researchers and consumers have noticed a decrease in quality when applying
NLP tools to minority variants of languages (ie Puerto Rican Spanish or Swiss German), but …