Dialogue summarization aims to condense the original dialogue into a shorter version
covering salient information, which is a crucial way to reduce dialogue data overload …

Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …

Developers deal with code-change-related tasks daily, eg, reviewing code. Pre-trained code
and code-change-oriented models have been adapted to help developers with such tasks …

Large foundation models, including large language models, vision transformers, diffusion,
and large language model based multimodal models, are revolutionizing the entire machine …

While large language models (LLMs) like ChatGPT have shown impressive capabilities in
Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this …

Abstract Out-of-distribution (OOD) detection, a fundamental task vexing real-world
applications, has attracted growing attention in the NLP community. Recently fine-tuning …

Label scarcity is a bottleneck for improving task performance in specialized domains. We
propose a novel compositional transfer learning framework (DoT5) for zero-shot domain …

Existing knowledge-grounded conversation systems generate responses typically in a
retrieve-then-generate manner. They require a large knowledge base and a strong …

Query-focused summarization has been considered as an important extension for text
summarization. It aims to generate a concise highlight for a given query. Different from text …

Traditional dialogue summarization models rely on a large-scale manually-labeled corpus,
lacking generalization ability to new domains, and domain adaptation from a labeled source …