collaborators

5 papers

cs.AI2026

PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs

Jiacheng Wang, Weiyan Zhang, Guangya Yu

Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes…

cs.AI2026

PsychēChat: An Empathic Framework Focused on Emotion Shift Tracking and Safety Risk Analysis in Psychological Counseling

Zhentao Xia, Yongqi Fan, Yuxiang Chu +4

Large language models (LLMs) have demonstrated notable advancements in psychological counseling. However, existing models generally do not explicitly model seekers' emotion shifts…

cs.CV2025

Can Multimodal Large Language Models Understand Spatial Relations?

Jingping Liu, Ziyan Liu, Zhedong Cen +5

Spatial relation reasoning is a crucial task for multimodal large language models (MLLMs) to understand the objective world. However, current benchmarks have issues like relying on…

cs.CL2025

CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation

Guangya Yu, Yanhao Li, Zongying Jiang +9

Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. With the impressive performance of large language mod…

cs.CL2025

LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review

Cheng Yuan, Xinkai Rui, Yongqi Fan +5

Despite the remarkable performance of Large Language Models (LLMs) in automated discharge summary generation, they still suffer from hallucination issues, such as generating inaccu…