Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

When groups of people are tasked with making a judgment, the issue of uncertainty often
arises. Existing methods to reduce uncertainty typically focus on iteratively improving …

Systems for making determinations on socially-constructed and complex concepts at scale
are increasingly being deployed. To make such fuzzy concepts tractable for training and …

Good datasets are a foundation of NLP research, and form the basis for training and
evaluating models of language use. While creating datasets, the standard practice is to …

Obtaining linguistic annotation from novice crowdworkers is far from trivial. A case in point is
the annotation of discourse relations, which is a complicated task. Recent methods have …

Preference-based learning aims to align robot task objectives with human values. One of the
most common methods to infer human preferences is by pairwise comparisons of robot task …

Extracting and formally representing the knowledge embedded in textbooks, such as the
concepts explained and the relations between them, can support the provision of advanced …

Many Natural Language Processing (NLP) systems use annotated corpora for training and
evaluation. However, labeled data is often costly to obtain and scaling annotation projects is …

Whether future AI models make the world safer or less safe for humans rests in part on our
ability to efficiently collect accurate data from people about what they want the models to do …

Large language models have transformed the field of natural language processing (NLP).
Their improved performance on various NLP benchmarks makes them a promising tool …