Large Language Models (LLMs) are frequently used for multi-faceted language generation
and evaluation tasks that involve satisfying intricate user constraints or taking into account …

With the recent undeniable advancement in reasoning abilities in large language models
(LLMs) like ChatGPT and GPT-4, there is a growing trend for using LLMs on various tasks …

Large Language Models (LLMs) are increasingly being used for interactive decision-making
tasks requiring planning and adapting to the environment. Recent works employ LLMs-as …

Language models (LMs) can perform complex reasoning either end-to-end, with hidden
latent state, or compositionally, with transparent intermediate state. Composition offers …

Humans ask follow-up questions driven by curiosity, which reflects a creative human
cognitive process. We introduce the task of real-world information-seeking follow-up …

To enhance the explainability of meeting summarization, we construct a new dataset called
“ExplainMeetSum,” an augmented version of QMSum, by newly annotating evidence …

While large language models (LLMs) are increasingly being used for program synthesis,
they lack the global view needed to develop useful abstractions; they generally predict …

Prompting large language models has enabled significant recent progress in multi-step
reasoning over text. However, when applied to text generation from semi-structured data …

Modern natural language generation paradigms require a good decoding strategy to obtain
quality sequences out of the model. Beam search yields high-quality but low diversity …

Abstractive summarization has made significant progress in recent years, which aims to
generate a concise and coherent summary that contains the most important facts from the …