A fundamental goal of scientific research is to learn about causal relationships. However,
despite its critical role in the life and social sciences, causality has not had the same …

Text style transfer is an important task in natural language generation, which aims to control
certain attributes in the generated text, such as politeness, emotion, humor, and many …

In this paper, we leverage large language models (LMs) to perform zero-shot text style
transfer. We present a prompting method that we call augmented zero-shot learning, which …

Large language models (LLMs) demonstrate emergent in-context learning capabilities,
where they adapt to new tasks based on example demonstrations. However, in-context …

Large Language Models (LLMs) have demonstrated impressive capabilities in creative tasks
such as storytelling and E-mail generation. However, as LLMs are primarily trained on final …

The reliance of text classifiers on spurious correlations can lead to poor generalization at
deployment, raising concerns about their use in safety-critical domains such as healthcare …

We present FRMT, a new dataset and evaluation benchmark for Few-shot Region-aware
Machine Translation, a type of style-targeted translation. The dataset consists of professional …

Software specifications are essential for ensuring the reliability of software systems. Existing
specification extraction approaches, however, suffer from limited generalizability and require …

Few shot learning with large language models has the potential to give individuals without
formal machine learning training the access to a wide range of text to text models. We …

Counterfactual generation lies at the core of various machine learning tasks, including
image translation and controllable text generation. This generation process usually requires …