Publications (9)
LightKGG: Simple and Efficient Knowledge Graph Generation from Textual Data
Teng Lin
The scarcity of high-quality knowledge graphs (KGs) remains a critical bottleneck for downstream AI applications, as existing extraction methods rely heavily on error-prone pattern…
Structure then Query: Enabling Precise Analytical Queries over Unstructured Documents
Teng Lin, Yuyu Luo, Nan Tang
Unstructured documents constitute the majority of enterprise and web data. With the rapid development of large language models(LLMs), researchers have started to build data systems…
SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graph
Teng Lin, Yizhang Zhu, Yuyu Luo +1
Multi-entity question answering (MEQA) poses significant challenges for large language models (LLMs), which often struggle to consolidate scattered information across multiple docu…
MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering
Teng Lin, Yuyu Luo, Honglin Zhang +4
Multi-entity question answering (MEQA) represents significant challenges for large language models (LLM) and retrieval-augmented generation (RAG) systems, which frequently struggle…
DataPuzzle: Breaking Free from the Hallucinated Promise of LLMs in Data Analysis
Zhengxuan Zhang, Zhuowen Liang, Yin Wu +3
Large language models (LLMs) are increasingly applied to multi-modal data analysis -- not necessarily because they offer the most precise answers, but because they provide fluent,…
Monte Carlo Tree Search for Table-to-Multimodal Report Generation
Teng Lin, Zhiyang Zhang, Yuyu Luo +1
Automatically generating professional multimodal reports comprising both textual analysis and visual charts from structured tabular data is a critical challenge in data intelligenc…