4 papers
Towards Proactive Detection of User-Side Implicit Conflicts in Human-LLM Dialogue
Jinqiang Wang, Tao Zhu, Huansheng Ning
In Human-LLM dialogue, follow-up user utterances may implicitly conflict with earlier intents, leading the LLM to misinterpret user needs and generate inappropriate responses. A re…
LLM-Driven Preference Data Synthesis for Proactive Prediction of the Next User Utterance in Human-Machine Dialogue
Jinqiang Wang, Huansheng Ning, Jianguo Ding +3
Proactively predicting a users next utterance in human-machine dialogue can streamline interaction and improve user experience. Existing commercial API-based solutions are subject…
A Data Synthesis Method Driven by Large Language Models for Proactive Mining of Implicit User Intentions in Tourism
Jinqiang Wang, Huansheng Ning, Tao Zhu +1
In the tourism domain, Large Language Models (LLMs) often struggle to mine implicit user intentions from tourists' ambiguous inquiries and lack the capacity to proactively guide us…
A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluations
Jinqiang Wang, Huansheng Ning, Yi Peng +5
Large Language Models (LLMs) have demonstrated surprising performance across various natural language processing tasks. Recently, medical LLMs enhanced with domain-specific knowled…