18 citations · 35 across the 10 of their papers we have counts for
10 papers
Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance
Peng Cui, Jitao Wang, Siyan Xue +40
Hepatocellular carcinoma (HCC) is a common malignancy and a leading cause of cancer-related mortality. Current guidelines and staging systems provide coarse categories, but often m…
AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
Peilin Wu, Xinlu Zhang, Kun Wan +4
Rubric-based reward shaping provides interpretable and editable reward signals for fine-tuning LLMs via reinforcement learning (RL), but existing adaptive rubric methods typically…
Principled RL for Flow Matching Emerges from the Chunk-level Policy Optimization
Yifu Luo, Haoyuan Sun, Xinhao Hu +12
Recent Progress in post-training flow matching for text-to-image (T2I) generation with Group Relative Policy Optimization (GRPO) has demonstrated strong potential. However, it is h…
Search Wisely: Mitigating Sub-optimal Agentic Searches By Reducing Uncertainty
Peilin Wu, Mian Zhang, Xinlu Zhang +2
Agentic Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by enabling dynamic, multi-step reasoning and information retrieval. However, these system…
Do Retrieval-Augmented Language Models Adapt to Varying User Needs?
Peilin Wu, Xinlu Zhang, Wenhao Yu +3
Recent advancements in Retrieval-Augmented Language Models (RALMs) have demonstrated their efficacy in knowledge-intensive tasks. However, existing evaluation benchmarks often assu…
CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy
Mian Zhang, Xianjun Yang, Xinlu Zhang +6
There is a significant gap between patient needs and available mental health support today. In this paper, we aim to thoroughly examine the potential of using Large Language Models…
Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
Xinlu Zhang, Zhiyu Zoey Chen, Xi Ye +4
Instruction Fine-Tuning (IFT) significantly enhances the zero-shot capabilities of pretrained Large Language Models (LLMs). While coding data is known to boost LLM reasoning abilit…
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
Ruiyi Wang, Stephanie Milani, Jamie C. Chiu +9
Mental illness remains one of the most critical public health issues. Despite its importance, many mental health professionals highlight a disconnect between their training and act…
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang +7
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: dom…
Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting
Zhiyu Chen, Yujie Lu, William Yang Wang
Mental illness remains one of the most critical public health issues of our time, due to the severe scarcity and accessibility limit of professionals. Psychotherapy requires high-l…