2025
![NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions [Experiment, Analysis & Benchmark]](/assets/images/covers/cover4.png)
NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions [Experiment, Analysis & Benchmark]
Shizheng Hou, Wenqi Pei, Nuo Chen, Quang-Trung Ta, Peng Lu, Beng Chin Ooi
VLDB 2026
We present NL2SQLBench, the first modular benchmarking framework for LLM-enabled NL2SQL approaches. We dissect NL2SQL systems into three modules: Schema Selection, Candidate Generation, and Query Revision. We review existing strategies and propose fine-grained metrics that systematically quantify module-level effectiveness and efficiency.
NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions [Experiment, Analysis & Benchmark]
Shizheng Hou, Wenqi Pei, Nuo Chen, Quang-Trung Ta, Peng Lu, Beng Chin Ooi
VLDB 2026
We present NL2SQLBench, the first modular benchmarking framework for LLM-enabled NL2SQL approaches. We dissect NL2SQL systems into three modules: Schema Selection, Candidate Generation, and Query Revision. We review existing strategies and propose fine-grained metrics that systematically quantify module-level effectiveness and efficiency.
Self-Monitoring Large Language Models for Click-Through Rate Prediction
Huachi Zhou, Kaijing Yu, Qinggang Zhang, Hao Chen, Daochen Zha, Wenqi Pei, Anthony Kong, Xiao Huang
ACM TOIS 2025
We propose Feature-Instructed Large language model for Monitoring (FILM), which introduces a self-monitoring temperature and an compaction loss. FILM not only improves feature utilization in LLMs but also enhances predictions for tail items.
Self-Monitoring Large Language Models for Click-Through Rate Prediction
Huachi Zhou, Kaijing Yu, Qinggang Zhang, Hao Chen, Daochen Zha, Wenqi Pei, Anthony Kong, Xiao Huang
ACM TOIS 2025
We propose Feature-Instructed Large language model for Monitoring (FILM), which introduces a self-monitoring temperature and an compaction loss. FILM not only improves feature utilization in LLMs but also enhances predictions for tail items.
InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback
Hengyuan Zhao*, Wenqi Pei*, Yifei Tao*, Haiyang Mei, Zheng Shou (* equal contribution)
EMNLP Finding & ICLR Bi-Align (Oral) 2025
This work introduces InterFeedback, a novel approach to understanding and enhancing the interactive intelligence of large multimodal models through human feedback mechanisms. Our framework provides new insights into model behavior and interaction patterns.
InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback
Hengyuan Zhao*, Wenqi Pei*, Yifei Tao*, Haiyang Mei, Zheng Shou (* equal contribution)
EMNLP Finding & ICLR Bi-Align (Oral) 2025
This work introduces InterFeedback, a novel approach to understanding and enhancing the interactive intelligence of large multimodal models through human feedback mechanisms. Our framework provides new insights into model behavior and interaction patterns.
Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models
Wenqi Pei, Hailing Xu, Hengyuan Zhao, Shizheng Hou, Han Chen, Zining Zhang, Pingyi Luo, Bingsheng He
IJCNLP-AACL Finding & ICLR DL4C 2025
We present Feather-SQL, a novel framework for Natural Language to Structured Query Language (NL2SQL) that leverages dual-model collaboration paradigm specifically designed for small language models. Our approach achieves state-of-the-art (SOTA) results on the BIRD benchmark.
Feather-SQL: A Lightweight NL2SQL Framework with Dual-Model Collaboration Paradigm for Small Language Models
Wenqi Pei, Hailing Xu, Hengyuan Zhao, Shizheng Hou, Han Chen, Zining Zhang, Pingyi Luo, Bingsheng He
IJCNLP-AACL Finding & ICLR DL4C 2025
We present Feather-SQL, a novel framework for Natural Language to Structured Query Language (NL2SQL) that leverages dual-model collaboration paradigm specifically designed for small language models. Our approach achieves state-of-the-art (SOTA) results on the BIRD benchmark.