37 citations · 111 across the 27 of their papers we have counts for
5 papers · 1 filter
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
Zhenyue Qin, Yu Yin, Dylan Campbell +6
The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment. Large vision-…
Unveiling Scoring Processes: Dissecting the Differences between LLMs and Human Graders in Automatic Scoring
Xuansheng Wu, Padmaja Pravin Saraf, Gyeonggeon Lee +3
Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans a…
Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering
Yucheng Shi, Qiaoyu Tan, Xuansheng Wu +3
Large Language Models (LLMs) have shown proficiency in question-answering tasks but often struggle to integrate real-time knowledge, leading to potentially outdated or inaccurate r…
Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era
Xuansheng Wu, Haiyan Zhao, Yaochen Zhu +9
Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended toward explaining…
InFoBench: Evaluating Instruction Following Ability in Large Language Models
Yiwei Qin, Kaiqiang Song, Yebowen Hu +7
This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models' (LLMs) ability to follow instructions. Addressing a gap…