2 citations · 3 across the 11 of their papers we have counts for
11 papers
ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models
Ruihui Hou, Siyi Zhu, Ziyue Huai +4
Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making scenarios. Existing benchma…
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…
Rank4Gen: RAG-Preference-Aligned Document Set Selection and Ranking
Yongqi Fan, Yuxiang Chu, Zhentao Xia +9
In the RAG paradigm, document ranking determines the evidence available to downstream generators. Through controlled analysis, we identify two phenomena underexplored by existing r…
KG-o1: Enhancing Multi-hop Question Answering in Large Language Models via Knowledge Graph Integration
Nan Wang, Yongqi Fan, yansha zhu +6
Large Language Models (LLMs) face challenges in knowledge-intensive reasoning tasks like classic multi-hop question and answering, which involves reasoning across multiple facts. T…
TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking
Yongqi Fan, Xiaoyang Chen, Dezhi Ye +6
Reasoning-intensive ranking models built on Large Language Models (LLMs) have made notable progress. However, existing approaches often rely on large-scale LLMs and explicit Chain-…
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…
MinosEval: Distinguishing Factoid and Non-Factoid for Tailored Open-Ended QA Evaluation with LLMs
Yongqi Fan, Yating Wang, Guandong Wang +4
Open-ended question answering (QA) is a key task for evaluating the capabilities of large language models (LLMs). Compared to closed-ended QA, it demands longer answer statements,…
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…
MSDiagnosis: A Benchmark for Evaluating Large Language Models in Multi-Step Clinical Diagnosis
Ruihui Hou, Shencheng Chen, Yongqi Fan +5
Clinical diagnosis is critical in medical practice, typically requiring a continuous and evolving process that includes primary diagnosis, differential diagnosis, and final diagnos…
MedOdyssey: A Medical Domain Benchmark for Long Context Evaluation Up to 200K Tokens
Yongqi Fan, Hongli Sun, Kui Xue +3
Numerous advanced Large Language Models (LLMs) now support context lengths up to 128K, and some extend to 200K. Some benchmarks in the generic domain have also followed up on evalu…
Tool Calling: Enhancing Medication Consultation via Retrieval-Augmented Large Language Models
Zhongzhen Huang, Kui Xue, Yongqi Fan +5
Large-scale language models (LLMs) have achieved remarkable success across various language tasks but suffer from hallucinations and temporal misalignment. To mitigate these shortc…