1 citations · 5 across the 19 of their papers we have counts for
19 papers
SEAL: Reinforcing Global Safety in Mixture-of-Experts through Shared Expert ALignment
Qingyu Meng, Yiwei Zha, Jiahuan Pei +3
Mixture-of-Experts (MoE) is a scaling architecture for large language models that activates only a small subset of expert modules per token, enabling massive parameter growth with…
When Trust Meets Truth: Trust-Truth Separability in LLM-as-Judge
Xin Sun, Di Wu, Yuchen Guo +4
LLM-as-Judge systems can produce multi-dimensional evaluations, such as trustworthiness, reliability, and factuality, and these outputs are often interpreted as independent evidenc…
MedEasy: Designing AI Standardized Patients for Clinical Consultation Training
Zhiqi Gao, Huarui Luo, Guo Zhu +6
AI standardized patients are becoming a setting for professional training in clinical consultation. This paper presents MedEasy, a multi-agent system that organizes virtual-patient…
SciText2Eq: Assessing LLMs for Explainable Equation Generation for Scientific Creativity
Yifan Mo, Xiao Fu, Yue Su +4
This work investigates the ability of large language models (LLMs) to generate mathematical equations from scientific texts. Prior work faces challenges in unstructured grounding,…
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional work…
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation
Qingyu Meng, Min Chen, Dingming Liu +5
Large language models (LLMs) can generate fluent dialogue, but prior works lack situational grounding, dynamic strategy control, and evaluation aligned with clinical standards in m…
Trust Stack for Mental Health AI: A Survey of Calibration across Human, Interaction, and AI Layers
Xin Sun, Yue Su, Yifan Mo +9
Language-based AI is increasingly deployed for mental health support, yet trust is evaluated in interdisciplinary but operationally misaligned ways: NLP and AI work measures robust…
NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners
Albert Tang, Yifan Mo, Jie Li +5
The double empathy problem frames communication difficulties between neurodivergent and neurotypical individuals as arising from mutual misunderstanding, yet most interventions foc…
"It Talks Like a Patient, But Feels Different": Co-Designing AI Standardized Patients with Medical Learners
Zhiqi Gao, Guo Zhu, Huarui Luo +6
Standardized patients (SPs) play a central role in clinical communication training but are costly, difficult to scale, and inconsistent. Large language model (LLM) based AI standar…
Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosis
Mithat Can Ozgun, Jiahuan Pei, Koen Hindriks +3
LLM-based agents have emerged as transformative tools capable of executing complex tasks through iterative planning and action, achieving significant advancements in understanding…
Talking-to-Build: How LLM-Assisted Interface Shapes Player Performance and Experience in Minecraft
Xin Sun, Lei Wang, Yue Li +5
With large language models (LLMs) on the rise, in-game interactions are shifting from rigid commands to natural conversations. However, the impacts of LLMs on player performance an…
MultiJustice: A Chinese Dataset for Multi-Party, Multi-Charge Legal Prediction
Xiao Wang, Jiahuan Pei, Diancheng Shui +4
Legal judgment prediction offers a compelling method to aid legal practitioners and researchers. However, the research question remains relatively under-explored: Should multiple d…
Conversational Education at Scale: A Multi-LLM Agent Workflow for Procedural Learning and Pedagogic Quality Assessment
Jiahuan Pei, Fanghua Ye, Xin Sun +3
Large language models (LLMs) have advanced virtual educators and learners, bridging NLP with AI4Education. Existing work often lacks scalability and fails to leverage diverse, larg…
LEGO Co-builder: Exploring Fine-Grained Vision-Language Modeling for Multimodal LEGO Assembly Assistants
Haochen Huang, Yue Su, Xin Sun +11
Vision-language models (VLMs) are facing the challenges of understanding and following multimodal assembly instructions, particularly when fine-grained spatial reasoning and precis…
Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy
Xin Sun, Jan de Wit, Zhuying Li +3
Chatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent…
A Comparative Analysis of Faithfulness Metrics and Humans in Citation Evaluation
Weijia Zhang, Mohammad Aliannejadi, Jiahuan Pei +3
Large language models (LLMs) often generate content with unsupported or unverifiable content, known as "hallucinations." To address this, retrieval-augmented LLMs are employed to i…
Rethinking the Alignment of Psychotherapy Dialogue Generation with Motivational Interviewing Strategies
Xin Sun, Xiao Tang, Abdallah El Ali +5
Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). How…
Assessing "Implicit" Retrieval Robustness of Large Language Models
Xiaoyu Shen, Rexhina Blloshmi, Dawei Zhu +2
Retrieval-augmented generation has gained popularity as a framework to enhance large language models with external knowledge. However, its effectiveness hinges on the retrieval rob…
Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics
Weijia Zhang, Mohammad Aliannejadi, Yifei Yuan +3
Large language models (LLMs) often produce unsupported or unverifiable content, known as "hallucinations." To mitigate this, retrieval-augmented LLMs incorporate citations, groundi…