181 citations · 350 across the 18 of their papers we have counts for
20 papers
Data Agents: Agentic Data Systems
Guoliang Li, Peiyao Zhou, Xuanhe Zhou +3
Traditional data systems face profound limitations in the AI era, relying on human-crafted pipelines, lacking semantic understanding of heterogeneous data, and operating through ri…
EcoTable: Cost-effective Table Integration in Data Lakes for Natural Language Queries
Yuhui Wang, Jinqi Liu, Chengliang Chai +8
The diverse formats of CSV and Parquet files in data lakes pose a significant challenge to traditional ETL, which relies on data engineers to pre-define a target database schema an…
Dial: A Knowledge-Grounded Dialect-Specific NL2SQL System
Xiang Zhang, Hongming Xu, Le Zhou +8
Enterprises commonly deploy heterogeneous database systems, each of which owns a distinct SQL dialect with different syntax rules, built-in functions, and execution constraints. Ho…
Data Agents: Levels, State of the Art, and Open Problems
Yuyu Luo, Guoliang Li, Ju Fan +1
Data agents are an emerging paradigm that leverages large language models (LLMs) and tool-using agents to automate data management, preparation, and analysis tasks. However, the te…
Reward-SQL: Boosting Text-to-SQL via Stepwise Execution-Aware Reasoning and Process-Supervised Rewards
Yuxin Zhang, Meihao Fan, Ju Fan +5
Recent advances in large language models (LLMs) trained with reinforcement learning (RL) have improved Text-to-SQL performance. However, RL-based approaches still struggle with com…
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…
Graph Neural Networks for Databases: A Survey
Ziming Li, Youhuan Li, Yuyu Luo +2
Graph neural networks (GNNs) are powerful deep learning models for graph-structured data, demonstrating remarkable success across diverse domains. Recently, the database (DB) commu…
Fine-Grained Knowledge Structuring and Retrieval for Visual Question Answering
Zhengxuan Zhang, Yin Wu, Yuyu Luo +1
Visual Question Answering (VQA) focuses on providing answers to natural language questions by utilizing information from images. Although cutting-edge multimodal large language mod…
RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition
Xudong Yang, Yizhang Zhu, Hanfeng Liu +3
Conventional Multi-modal multi-label emotion recognition (MMER) assumes complete access to visual, textual, and acoustic modalities. However, real-world multi-party settings often…
ChartEditor: A Human-AI Paired Tool for Authoring Pictorial Charts
Siyu Yan, Tiancheng Liu, Weikai Yang +2
Pictorial charts are favored for their memorability and visual appeal, offering a more engaging alternative to basic charts. However, their creation can be complex and time-consumi…
AskChart: Universal Chart Understanding through Textual Enhancement
Xudong Yang, Yifan Wu, Yizhang Zhu +2
Chart understanding tasks such as ChartQA and Chart-to-Text involve automatically extracting and interpreting key information from charts, enabling users to query or convert visual…
SketchFill: Sketch-Guided Code Generation for Imputing Derived Missing Values
Yunfan Zhang, Changlun Li, Yuyu Luo +1
Missing value is a critical issue in data science, significantly impacting the reliability of analyses and predictions. Missing value imputation (MVI) is a longstanding problem bec…
A Plug-and-Play Natural Language Rewriter for Natural Language to SQL
Peixian Ma, Boyan Li, Runzhi Jiang +3
Existing Natural Language to SQL (NL2SQL) solutions have made significant advancements, yet challenges persist in interpreting and translating NL queries, primarily due to users' l…
A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?
Xinyu Liu, Shuyu Shen, Boyan Li +7
Translating users' natural language queries (NL) into SQL queries (i.e., Text-to-SQL, a.k.a. NL2SQL) can significantly reduce barriers to accessing relational databases and support…
The Dawn of Natural Language to SQL: Are We Fully Ready?
Boyan Li, Yuyu Luo, Chengliang Chai +2
Translating users' natural language questions into SQL queries (i.e., NL2SQL) significantly lowers the barriers to accessing relational databases. The emergence of Large Language M…
HAIChart: Human and AI Paired Visualization System
Yupeng Xie, Yuyu Luo, Guoliang Li +1
The growing importance of data visualization in business intelligence and data science emphasizes the need for tools that can efficiently generate meaningful visualizations from la…
Are Large Language Models Good Statisticians?
Yizhang Zhu, Shiyin Du, Boyan Li +2
Large Language Models (LLMs) have demonstrated impressive capabilities across a range of scientific tasks including mathematics, physics, and chemistry. Despite their successes, th…
ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
Yifan Wu, Lutao Yan, Leixian Shen +3
Chart question answering (ChartQA) tasks play a critical role in interpreting and extracting insights from visualization charts. While recent advancements in multimodal large langu…
nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task
Yuyu Luo, Jiawei Tang, Guoliang Li
NL2VIS - which translates natural language (NL) queries to corresponding visualizations (VIS) - has attracted more and more attention both in commercial visualization vendors and a…
Towards Natural Language Interfaces for Data Visualization: A Survey
Leixian Shen, Enya Shen, Yuyu Luo +5
Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user expe…