We introduce a method for improving the structural understanding abilities of language
models. Unlike previous approaches that finetune the models with task-specific …

This is the first survey of the active area of AI research that focuses on privacy issues in
Large Language Models (LLMs). Specifically, we focus on work that red-teams models to …

We introduce GEM, a living benchmark for natural language Generation (NLG), its
Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving …

The concept of persona, originally adopted in dialogue literature, has re-surged as a
promising framework for tailoring large language models (LLMs) to specific context (eg …

Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing
approaches for DST mainly fall into one of two categories, namely, ontology-based and …

Task-oriented dialogue (TOD) models have made significant progress in recent years.
However, previous studies primarily focus on datasets written by annotators, which has …

The MultiWOZ 2.0 dataset has greatly stimulated the research of task-oriented dialogue
systems. However, its state annotations contain substantial noise, which hinders a proper …

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented
dialogue in unseen domains without the expense of collecting in-domain data. In this paper …