The task of zero-shot commonsense question answering evaluates models on their capacity
to reason about general scenarios beyond those presented in specific datasets. Existing …

Conceptualization, or viewing entities and situations as instances of abstract concepts in
mind and making inferences based on that, is a vital component in human intelligence for …

Human behavior simulation of AI agents necessitates the agents to possess a quality of
believability, which is crucial as it facilitates users in establishing trust toward the agents and …

Existing works on long-term open-domain dialogues focus on evaluating model responses
within contexts spanning no more than five chat sessions. Despite advancements in long …

Humans share a wide variety of images related to their personal experiences within
conversations via instant messaging tools. However, existing works focus on (1) image …

The tremendous development in large language models (LLM) has led to a new wave of
innovations and applications and yielded research results that were initially forecast to take …

Persona-grounded dialogue systems aim to enhance the quality of AI agent responses by
bolstering persona consistency and promoting response diversity. Although model tuning …

We propose a new benchmark, ComperDial, which facilitates the training and evaluation of
evaluation metrics for open-domain dialogue systems. ComperDial consists of human …

Inferring contextually-relevant and diverse commonsense to understand narratives remains
challenging for knowledge models. In this work, we develop a series of knowledge models …

In the persona-grounded dialogue (PGD) task, it is required not only to respond fluently, but
also to ground the attributes according to the current conversation topic properly. However …