4 papers · 1 filter
JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation
Zhichao Shi, Xuhui Jiang, Chengjin Xu +6
Current evaluation methods for large language models (LLMs) primarily rely on static benchmarks, presenting two major challenges: limited knowledge coverage and fixed difficulties…
Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject
Zenghao Duan, Wenbin Duan, Zhiyi Yin +5
Knowledge editing has become a promising approach for efficiently and precisely updating knowledge embedded in large language models (LLMs). In this work, we focus on Same-Subject…
A Survey on LLM-as-a-Judge
Jiawei Gu, Xuhui Jiang, Zhichao Shi +13
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. La…
Unlocking the Power of Large Language Models for Entity Alignment
Xuhui Jiang, Yinghan Shen, Zhichao Shi +6
Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on co…