  Recently, there has been a heightened interest in building chatbots based on
Large Language Models (LLMs) to emulate human-like qualities in dialogues,
including expressing empathy and offering emotional support. Despite having
access to commonsense knowledge to better understand the psychological aspects
and causality of dialogue context, even these powerful LLMs struggle to achieve
the goals of empathy and emotional support. As current approaches do not
adequately anticipate dialogue future, they may mislead language models to
ignore complex dialogue goals of empathy and emotional support, resulting in
unsupportive responses lacking empathy. To address this issue, we present an
innovative framework named Sensible Empathetic Dialogue Generation with
Visionary Commonsense Knowledge (Sibyl). Designed to concentrate on the
imminent dialogue future, this paradigm directs LLMs toward the implicit
requirements of the conversation, aiming to provide more sensible responses.
Experimental results demonstrate that incorporating our paradigm for acquiring
commonsense knowledge into LLMs comprehensively enhances the quality of their
responses.
