Despite widespread use of LLMs as conversational agents, evaluations of performance fail
to capture a crucial aspect of communication: interpreting language in context …

In this paper, we propose a novel task IntentQA, a special VideoQA task focusing on video
intent reasoning, which has become increasingly important for AI with its advantages in …

Pragmatics and non-literal language understanding are essential to human communication,
and present a long-standing challenge for artificial language models. We perform a fine …

The recent success of large language models (LLMs) has drawn heavy attention and
investment in their use as conversational and embodied systems. Despite widespread use …

The ability to discern and comprehend pragmatic meanings is a cornerstone of social and
emotional intelligence, referred to as pragmatic reasoning. Despite the strides made in the …

Humans often express their communicative intents indirectly or non-literally, which requires
their interlocutors—human or AI—to understand beyond the literal meaning of words. While …

Understanding the non-literal meaning of an utterance is critical for large language models
(LLMs) to become human-like social communicators. In this work, we introduce …

Questions posed by information-seeking users often contain implicit false or potentially
harmful assumptions. In a high-risk domain such as maternal and infant health, a question …

Much of human communication depends on implication, conveying meaning beyond literal
words to express a wider range of thoughts, intentions, and feelings. For models to better …

Humans often express their communicative intents indirectly or non-literally, which requires
their interlocutors--human or AI--to understand beyond the literal meaning of words. While …