1 citations · 1 across the 2 of their papers we have counts for
9 papers
DTBench: A Synthetic Benchmark for Document-to-Table Extraction
Yuxiang Guo, Zhuoran Du, Nan Tang +3
Document-to-table (Doc2Table) extraction derives structured tables from unstructured documents under a target schema, enabling reliable and verifiable SQL-based data analytics. Alt…
Structural and Disentangled Adaptation of Large Vision Language Models for Multimodal Recommendation
Zhongtao Rao, Peilin Zhou, Dading Chong +3
Multimodal recommendation enhances accuracy by leveraging visual and textual signals, and its success largely depends on learning high-quality cross-modal representations. Recent a…
CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research?
Xiangsen Chen, Xuan Feng, Shuo Chen +5
Analyzing Open Source Intelligence (OSINT) from large volumes of data is critical for drafting and publishing comprehensive CTI reports. This process usually follows a three-stage…
Text2GraphQuery-Bench: A Text to Graph Query Benchmark
Songlin Lyu, Lujie Ban, Zihang Wu +14
Graph models are fundamental to data analysis in domains rich with complex relationships. Unlike SQL, which benefits from a rel- atively unified standard and widespread familiarity…
LakeHopper: Knowledge-Aware Adaptation of Column Type Annotators across Data Lakes
Yushi Sun, Xujia Li, Nan Tang +3
Column Type Annotation (CTA), which assigns a semantic type to a table column, underpins data integration, cleaning, and search over data lakes. State-of-the-art annotators are pre…
KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Yushi Sun, Kai Sun, Yifan Ethan Xu +4
Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial informatio…