1 citations · 6 across the 13 of their papers we have counts for
13 papers
DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces
Boyan Li, Zhuowen Liang, Yupeng Xie +11
Data agents enable natural-language analytics over organizational workspaces, where relevant evidence may be scattered across databases, structured files, long documents, and multi…
SQLConductor: Search-to-Policy Learning for Step-wise Text-to-SQL Orchestration
Yizhang Zhu, Zhangyang Peng, Boyan Li +1
Text-to-SQL enables users to access relational databases via natural language, but real-world settings remain challenging due to coordinated reasoning over complex database environ…
DeepEye: A Steerable Self-driving Data Agent System
Boyan Li, Yiran Peng, Yupeng Xie +5
Large Language Models (LLMs) have revolutionized natural language interaction with data. The "holy grail" of data analytics is to build autonomous Data Agents that can self-drive c…
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…
OmniMoE: An Efficient MoE by Orchestrating Atomic Experts at Scale
Jingze Shi, Zhangyang Peng, Yizhang Zhu +3
Mixture-of-Experts (MoE) architectures are evolving towards finer granularity to improve parameter efficiency. However, existing MoE designs face an inherent trade-off between the…
A Survey of Data Agents: Emerging Paradigm or Overstated Hype?
Yizhang Zhu, Liangwei Wang, Chenyu Yang +22
The rapid advancement of large language models (LLMs) has spurred the emergence of data agents, autonomous systems designed to orchestrate Data + AI ecosystems for tackling complex…
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning
Xiaotian Lin, Yanlin Qi, Yizhang Zhu +4
Instruction tuning has emerged as a critical paradigm for improving the capabilities and alignment of large language models (LLMs). However, existing iterative model-aware data sel…
Boosting Text-to-Chart Retrieval through Training with Synthesized Semantic Insights
Yifan Wu, Lutao Yan, Yizhang Zhu +6
Text-to-chart retrieval, enabling users to find relevant charts via natural language queries, has gained significant attention. However, evaluating models in real-world business in…
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…
EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing
Yizhang Zhu, Runzhi Jiang, Boyan Li +2
Text-to-SQL automatically translates natural language queries to SQL, allowing non-technical users to retrieve data from databases without specialized SQL knowledge. Despite the su…
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…
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…
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…