3 citations · 8 across the 10 of their papers we have counts for
12 papers
CSWBench: Evaluating Context-Sensitive Code-Switching in Creative Writing
Zewen Jiang, Yahan Li, Chenlu Guo +3
Large language models (LLMs) have demonstrated multilingual capabilities across diverse language generation and understanding tasks. Yet effective code-switching requires more than…
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks
Lukas Niehaus, Robin Rawiel, Ulf Krumnack
KANLab is a browser-based visualization tool designed to interactively teach the core concepts of Kolmogorov-Arnold Networks (KAN), forked from the widely successful TensorFlow Pla…
MedExAgent: Training LLM Agents to Ask, Examine, and Diagnose in Noisy Clinical Environments
Yicheng Gao, Xiaolin Zhou, Yahan Li +2
Real-world clinical diagnosis is a complex process in which the doctor is required to obtain information from both interaction with the patient and conducting medical exams. Additi…
CounselReflect: Opportunities and Challenges for Designing Tools to Support Self-Reflection on Mental Health and Well-Being Conversations with AI
Yahan Li, Chaohao Du, Zeyang Li +7
AI is increasingly used for mental health and well-being support, creating an urgent need for safer engagement, while design, evaluation, and governance take time to develop. We ex…
Beyond Idealized Patients: Evaluating LLMs under Challenging Patient Behaviors in Medical Consultations
Yahan Li, Xinyi Jie, Wanjia Ruan +5
Large language models (LLMs) are increasingly used for medical consultation and health information support, where safety depends not only on medical knowledge but also on robust re…
VoiceAgentEval: A Dual-Dimensional Benchmark for Expert-Level Intelligent Voice-Agent Evaluation of Xbench's Professional-Aligned Series
Pengyu Xu, Shijia Li, Ao Sun +15
We propose OutboundEval, a comprehensive benchmark for evaluating large language models (LLMs) in expert-level intelligent outbound calling scenarios. Unlike existing methods that…
Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing
Ziyu Fan, Zhijian Huang, Yahan Li +9
Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between mo…
CounselBench: A Large-Scale Expert Evaluation and Adversarial Benchmarking of Large Language Models in Mental Health Question Answering
Yahan Li, Jifan Yao, John Bosco S. Bunyi +3
Medical question answering (QA) benchmarks often focus on multiple-choice or fact-based tasks, leaving open-ended answers to real patient questions underexplored. This gap is parti…
Are Clinical T5 Models Better for Clinical Text?
Yahan Li, Keith Harrigian, Ayah Zirikly +1
Large language models with a transformer-based encoder/decoder architecture, such as T5, have become standard platforms for supervised tasks. To bring these technologies to the cli…
Large Language Model Evaluation via Matrix Nuclear-Norm
Yahan Li, Tingyu Xia, Yi Chang +1
As large language models (LLMs) continue to evolve, efficient evaluation metrics are vital for assessing their ability to compress information and reduce redundancy. While traditio…
XTRUST: On the Multilingual Trustworthiness of Large Language Models
Yahan Li, Yi Wang, Yi Chang +1
Large language models (LLMs) have demonstrated remarkable capabilities across a range of natural language processing (NLP) tasks, capturing the attention of both practitioners and…
Vision Transformer-based Adversarial Domain Adaptation
Yahan Li, Yuan Wu
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. The most recent UDA methods always resort to adversarial…