1 citations · 4 across the 11 of their papers we have counts for
12 papers
Structured Composition of Verifiable Atomic Insights for Table-to-Report Generation
Teng Lin, Xinyu Liu, Nan Tang
Table-to-report generation refers to the task of automatically generating article-level analyt- ical reports from relational tables and is an essential capability for automated dat…
EidosDoc: Implicit Structure Encoding for Cost-Effective Semi-Structured Document QA
Teng Lin, Yuyu Luo, Nan Tang
Semi-structured documents are ubiquitous in scientific reports, financial statements, and technical manuals. Question answering over such documents requires simultaneous understand…
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…
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…
AnnoRetrieve: Efficient Structured Retrieval for Unstructured Document Analysis
Teng Lin, Yuyu Luo, Nan Tang
Unstructured documents dominate enterprise and web data, but their lack of explicit organization hinders precise information retrieval. Current mainstream retrieval methods, especi…
Memory in the LLM Era: Modular Architectures and Strategies in a Unified Framework
Yanchen Wu, Tenghui Lin, Yingli Zhou +7
Memory emerges as the core module in the large language model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…
DocSage: An Information Structuring Agent for Multi-Doc Multi-Entity Question Answering
Teng Lin, Yizhang Zhu, Zhengxuan Zhang +2
Multi-document Multi-entity Question Answering inherently demands models to track implicit logic between multiple entities across scattered documents. However, existing Large Langu…
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…
Simplifying Data Integration: SLM-Driven Systems for Unified Semantic Queries Across Heterogeneous Databases
Teng Lin
The integration of heterogeneous databases into a unified querying framework remains a critical challenge, particularly in resource-constrained environments. This paper presents a…
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,…
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…