118 citations · 137 across the 17 of their papers we have counts for
17 papers
Stellar: Scalable Multimodal Document Retrieval for Natural Language Queries
Yuxiang Guo, Zhonghao Hu, Yuren Mao +5
Multimodal document retrieval--selecting the most relevant multimodal document from a large corpus to answer a natural language query--plays an essential role in Retrieval-Augmente…
LASER: A Data-Centric Method for Low-Cost and Efficient SQL Rewriting based on SQL-GRPO
Jiahui Li, Tongwang Wu, Yuren Mao +3
Query rewriting, the process of transforming queries into semantically equivalent yet more efficient variants, is crucial for database optimization. Existing solutions predominantl…
Unbiased Multimodal Reranking for Long-Tail Short-Video Search
Wenyi Xu, Feiran Zhu, Songyang Li +6
Kuaishou serving hundreds of millions of searches daily, the quality of short-video search is paramount. However, it suffers from a severe Matthew effect on long-tail queries: spar…
OpenHospital: A Thing-in-itself Arena for Evolving and Benchmarking LLM-based Collective Intelligence
Peigen Liu, Rui Ding, Yuren Mao +7
Large Language Model (LLM)-based Collective Intelligence (CI) presents a promising approach to overcoming the data wall and continuously boosting the capabilities of LLM agents. Ho…
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…
Can We Predict Before Executing Machine Learning Agents?
Jingsheng Zheng, Jintian Zhang, Yujie Luo +5
Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer from a…
Agent-Kernel: A MicroKernel Multi-Agent System Framework for Adaptive Social Simulation Powered by LLMs
Yuren Mao, Peigen Liu, Xinjian Wang +11
Multi-Agent System (MAS) developing frameworks serve as the foundational infrastructure for social simulations powered by Large Language Models (LLMs). However, existing frameworks…
CT-Agent: A Multimodal-LLM Agent for 3D CT Radiology Question Answering
Yuren Mao, Wenyi Xu, Yuyang Qin +1
Computed Tomography (CT) scan, which produces 3D volumetric medical data that can be viewed as hundreds of cross-sectional images (a.k.a. slices), provides detailed anatomical info…
Text-to-Pipeline: Bridging Natural Language and Data Preparation Pipelines
Yuhang Ge, Yachuan Liu, Zhangyan Ye +2
Data preparation (DP) transforms raw data into a form suitable for downstream applications, typically by composing operations into executable pipelines. Building such pipelines is…
Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index
Yuxiang Guo, Zhonghao Hu, Yuren Mao +3
Natural language (NL)-driven table discovery identifies relevant tables from large table repositories based on NL queries. While current deep-learning-based methods using the tradi…
scAgent: Universal Single-Cell Annotation via a LLM Agent
Yuren Mao, Yu Mi, Peigen Liu +3
Cell type annotation is critical for understanding cellular heterogeneity. Based on single-cell RNA-seq data and deep learning models, good progress has been made in annotating a f…
SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation
Jiahui Li, Tongwang Wu, Yuren Mao +3
High quality SQL corpus is essential for intelligent database. For example, Text-to-SQL requires SQL queries and correspond natural language questions as training samples. However,…
DAgent: A Relational Database-Driven Data Analysis Report Generation Agent
Wenyi Xu, Yuren Mao, Xiaolu Zhang +4
Relational database-driven data analysis (RDB-DA) report generation, which aims to generate data analysis reports after querying relational databases, has been widely applied in fi…
Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns
Yuxiang Guo, Yuren Mao, Zhonghao Hu +2
Semantic join discovery, which aims to find columns in a table repository with high semantic joinabilities to a query column, is crucial for dataset discovery. Existing methods can…
Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents
Yuyou Gan, Yong Yang, Zhe Ma +10
With the continuous development of large language models (LLMs), transformer-based models have made groundbreaking advances in numerous natural language processing (NLP) tasks, lea…
A Survey on LoRA of Large Language Models
Yuren Mao, Yuhang Ge, Yijiang Fan +4
Low-Rank Adaptation~(LoRA), which updates the dense neural network layers with pluggable low-rank matrices, is one of the best performed parameter efficient fine-tuning paradigms.…
FIT-RAG: Black-Box RAG with Factual Information and Token Reduction
Yuren Mao, Xuemei Dong, Wenyi Xu +3
Due to the extraordinarily large number of parameters, fine-tuning Large Language Models (LLMs) to update long-tail or out-of-date knowledge is impractical in lots of applications.…