Natural language generation has witnessed significant advancements due to the training of
large language models on vast internet-scale datasets. Despite these advancements, there …

Many real-world applications of language models (LMs), such as writing assistance and
code autocomplete, involve human-LM interaction. However, most benchmarks are non …

We present Step-Back Prompting, a simple prompting technique that enables LLMs to do
abstractions to derive high-level concepts and first principles from instances containing …

Recent advances of powerful Language Models have allowed Natural Language
Generation (NLG) to emerge as an important technology that can not only perform traditional …

With language models becoming increasingly ubiquitous, it has become essential to
address their inequitable treatment of diverse demographic groups and factors. Most …

The latest batch of research has equipped language models with the ability to attend over
relevant and factual information from non-parametric external sources, drawing a …

Task semantics can be expressed by a set of input-output examples or a piece of textual
instruction. Conventional machine learning approaches for natural language processing …

Machine learning models have been trained to predict semantic information about user
interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently …

Data quality is crucial for training accurate, unbiased, and trustworthy machine learning
models as well as for their correct evaluation. Recent work, however, has shown that even …

The rapid advancement of large language models (LLMs) has sparked interest in data
synthesis techniques, aiming to generate diverse and high-quality synthetic datasets …