2 citations · 4 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
SQL-to-Text Generation with Weighted-AST Few-Shot Prompting
Sriom Chakrabarti, Chuangtao Ma, Arijit Khan +1
SQL-to-Text generation aims at translating structured SQL queries into natural language descriptions, thereby facilitating comprehension of complex database operations for non-tech…
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
Chuangtao Ma, Yongrui Chen, Tianxing Wu +2
Large language models (LLMs) have demonstrated remarkable performance on question-answering (QA) tasks because of their superior capabilities in natural language understanding and…
Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
Chuangtao Ma, Sriom Chakrabarti, Arijit Khan +1
Traditional similarity-based schema matching methods are incapable of resolving semantic ambiguities and conflicts in domain-specific complex mapping scenarios due to missing commo…
LLM+KG@VLDB'24 Workshop Summary
Arijit Khan, Tianxing Wu, Xi Chen
The unification of large language models (LLMs) and knowledge graphs (KGs) has emerged as a hot topic. At the LLM+KG'24 workshop, held in conjunction with VLDB 2024 in Guangzhou, C…