3 papers
cs.CL2026
An evidence-guided reinforcement learning method to improve psychiatric reasoning in small language models
Xinxin Lin, Guangxin Dai, Yi Zhong +25
Privacy and computational constraints limit the use of large language models in psychiatry, while adapting small language models (SLMs) often requires substantial data and expert a…
cs.AI2025
Enhancing the Capabilities of Large Language Models for API calls through Knowledge Graphs
Ye Yang, Xue Xiao, Ping Yin +1
API calls by large language models (LLMs) offer a cutting-edge approach for data analysis. However, their ability to effectively utilize tools via API calls remains underexplored i…
cs.CV2025
Learning Heterogeneous Mixture of Scene Experts for Large-scale Neural Radiance Fields
Zhenxing Mi, Ping Yin, Xue Xiao +1
Recent NeRF methods on large-scale scenes have underlined the importance of scene decomposition for scalable NeRFs. Although achieving reasonable scalability, there are several cri…