Hello, I am Yunze — a research intern at Hunyuan, Tencent, and a graduate student at the National University of Singapore. I build and study learning systems that reason, evaluate, and improve.
Selected research
We identify two fundamental inconsistencies in LLM-as-a-judge evaluation: Score-Comparison inconsistency and Pairwise Transitivity inconsistency. We propose TrustJudge, a probabilistic framework with distribution-sensitive scoring and likelihood-aware aggregation to alleviate these issues and improve reliability of automated assessment.
Two major obstacles hinder RAG's progress: (1) lack of thorough and fair comparisons between emerging RAG algorithms; (2) high-level abstractions in open-source tools reduce transparency and limit innovation. We introduce RAGLAB, a modular, research-focused open-source library that replicates existing algorithms and provides an ecosystem for exploring RAG techniques, enabling fair comparisons across benchmarks.
Speaking
One continuous route
2025.12 – now
Shenzhen, China
2025.01 – 2027.02
National University of Singapore, Singapore Temasek Laboratories @ NUS, Singapore
2023.11 – 2024.11
Beijing, China
2023.05 – 2023.09
Hangzhou, China
2019.09 – 2023.07
Xi'an Jiaotong-Liverpool University, Suzhou, China University of Liverpool, United Kingdom
