Grice’s Quantity Maxims dictate that human speakers aim for the optimal quantity of information during conversation. To empower LLMs to self-repair their responses toward optimal quantity and improve their attentive listening skills, we propose Q-Tuning and Q-Traveling, which draw on heuristic path-finding to enable decoder-only LLMs to travel among multiple “Q-alternatives” (Quantity Alternatives) and search for the optimal quantity in coordination with a conversation goal. Automatic and human evaluations demonstrate the effectiveness of Q-Tuning and Q-Traveling in constructing human-like, user-centered conversation agents.

Human empathy builds on the shared pragmatic common ground among different languages. However, existing human empathy data is limited to English. Inspired by multilingual coactivation as the neurocognitive underpinning of human bilingual proficiency, which predicts empathy, we integrate language-independent diffusion processes to facilitate the cross-lingual transfer of empathy. Taking Chinese language varieties as the target domain, automatic and human evaluations demonstrate successful transfers of source empathy into target contexts without compromising linguistic naturalness. The results of this work offer empirical clues on the importance of pragmatic transferability of empathy and its cross-lingual effects in conversation.