The rapid advancement of chat-based language models has led to remarkable progress in
complex task-solving. However, their success heavily relies on human input to guide the …

Chat models, such as ChatGPT, have shown impressive capabilities and have been rapidly
adopted across numerous domains. However, these models are only accessible through a …

In the rapidly evolving field of large language models (LLMs), data augmentation (DA) has
emerged as a pivotal technique for enhancing model performance by diversifying training …

With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …

We present SODA: the first publicly available, million-scale high-quality social dialogue
dataset. Using SODA, we train COSMO: a generalizable conversation agent outperforming …

Integrating free-text explanations to in-context learning of large language models (LLM) is
shown to elicit strong reasoning capabilities along with reasonable explanations. In this …

With direct access to human-written reference as memory, retrieval-augmented generation
has achieved much progress in a wide range of text generation tasks. Since better memory …

The success of Large Language Models (LLMs) is inherently linked to the availability of vast,
diverse, and high-quality data for training and evaluation. However, the growth rate of high …

Building end-to-end task bots and maintaining their integration with new functionalities using
minimal human efforts is a long-standing challenge in dialog research. Recently large …

Dialogue systems and large language models (LLMs) have gained considerable attention.
However, the direct utilization of LLMs as task-oriented dialogue (TOD) models has been …