4 citations · 9 across the 9 of their papers we have counts for
10 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…
CTourLLM: Enhancing LLMs with Chinese Tourism Knowledge
Qikai Wei, Mingzhi Yang, Jinqiang Wang +3
Recently, large language models (LLMs) have demonstrated their effectiveness in various natural language processing (NLP) tasks. However, the lack of tourism knowledge limits the p…
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…
An Improved Masking Strategy for Self-supervised Masked Reconstruction in Human Activity Recognition
Jinqiang Wang, Tao Zhu, Huansheng Ning
Masked reconstruction serves as a fundamental pretext task for self-supervised learning, enabling the model to enhance its feature extraction capabilities by reconstructing the mas…
A Multi-Task Deep Learning Approach for Sensor-based Human Activity Recognition and Segmentation
Furong Duan, Tao Zhu, Jinqiang Wang +3
Sensor-based human activity segmentation and recognition are two important and challenging problems in many real-world applications and they have drawn increasing attention from th…
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition
Jinqiang Wang, Tao Zhu, Liming Chen +2
Contrastive learning has been applied to Human Activity Recognition (HAR) based on sensor data owing to its ability to achieve performance comparable to supervised learning with a…
Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data
Rui Hu, Jitao Sang, Jinqiang Wang +1
Deep network models perform excellently on In-Distribution (ID) data, but can significantly fail on Out-Of-Distribution (OOD) data. While developing methods focus on improving OOD…
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition
Jinqiang Wang, Tao Zhu, Jingyuan Gan +3
While deep learning has contributed to the advancement of sensor-based Human Activity Recognition (HAR), it is usually a costly and challenging supervised task with the needs of a…