Neural networks for NLP are becoming increasingly complex and widespread, and there is a
growing concern if these models are responsible to use. Explaining models helps to address …

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

State-of-the-art neural language models can now be used to solve ad-hoc language tasks
through zero-shot prompting without the need for supervised training. This approach has …

Large Language Models (LLMs) have gained widespread popularity due to their ability to
perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language …

Practitioners increasingly use machine learning (ML) models, yet models have become
more complex and harder to understand. To understand complex models, researchers have …

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 …

Despite impressive performance on standard benchmarks, deep neural networks are often
brittle when deployed in real-world systems. Consequently, recent research has focused on …

In this paper, we present a natural language code synthesis tool, GenLine, backed by 1) a
large generative language model and 2) a set of task-specific prompts that create or change …

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and
free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and …

Although AI holds promise for improving human decision making in societally critical
domains, it remains an open question how human-AI teams can reliably outperform AI alone …