8 citations · 9 across the 10 of their papers we have counts for
10 papers
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…
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…
Text2GraphQuery-Bench: A Text to Graph Query Benchmark
Songlin Lyu, Lujie Ban, Zihang Wu +12
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…
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…
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…
SCAR: A Characterization Scheme for Multi-Modal Dataset
Ri Su, Zhao Chen, Caleb Chen Cao +2
Foundation models exhibit remarkable generalization across diverse tasks, largely driven by the characteristics of their training data. Recent data-centric methods like pruning and…
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability
Xiangsen Chen, Xuming Hu, Nan Tang
Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incor…
Are Large Language Models a Good Replacement of Taxonomies?
Yushi Sun, Hao Xin, Kai Sun +5
Large language models (LLMs) demonstrate an impressive ability to internalize knowledge and answer natural language questions. Although previous studies validate that LLMs perform…
CRAG -- Comprehensive RAG Benchmark
Xiao Yang, Kai Sun, Hao Xin +24
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets,…