20 citations · 20 across the 2 of their papers we have counts for
3 papers
cs.AI2026
BrailleBench: Investigating Multi-Criteria Braille Comprehension in Large Language Models
Jinghan Zhang, Fengran Mo, Zhiyu Chen +3
Although Large language models (LLMs) mediate access to knowledge and computational assistance, their capabilities should benefit vulnerable groups in the same way. However, it is…
cs.CL2026
GeoRisk-RAG: A Hierarchy-Aware Risk Framework for Improving RAG Reliability through Selective Answering
Meenu Ravi, Shailik Sarkar, Lulwah AlKulaib +2
Current work on improving reliability in large language model (LLM)- generated answers has primarily leveraged Retrieval-Augmented Generation (RAG), knowledge-graph augmentation, a…
cs.LG2024★ 20 cited
A Comprehensive Survey on Data Augmentation
Zaitian Wang, Pengfei Wang, Kunpeng Liu +6
Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models…