2 citations · 4 across the 7 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.DB2026
Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG
Chuangtao Ma, Arijit Khan
Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant ac…
cs.DB2026
LLM+Graph@VLDB'2025 Workshop Summary
Yixiang Fang, Arijit Khan, Tianxing Wu +2
The integration of large language models (LLMs) with graph-structured data has become a pivotal and fast evolving research frontier, drawing strong interest from both academia and…
cs.DB2026
Cost-Efficient RAG for Entity Matching with LLMs: A Blocking-based Batch Processing Framework
Chuangtao Ma, Zeyu Zhang, Arijit Khan +2
Large language models (LLMs) offer a compelling approach to entity matching (EM) in label-scarce settings, yet their matching quality is sensitive to records with ambiguous or spar…