From the 1 of 7 linked papers with an AI index.
2 citations · 2 across the 3 of their papers we have counts for
7 papers
QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL
Yinggang Sun, Ziming Guo, Haining Yu +5
The paper introduces QDA-SQL, a data augmentation technique that uses large language models to generate and validate multi‑turn question‑answer pairs, improving fine‑tuned models'…
Adapt to Thrive! Adaptive Power-Mean Policy Optimization for Improved LLM Reasoning
Yiming Huang, Zhenbo Shi, Shuzheng Gao +3
Reinforcement Learning with Verifiable Rewards (RLVR) is an essential paradigm that enhances the reasoning capabilities of Large Language Models (LLMs). However, existing methods t…
Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs
Yiming Huang, Zhenbo Shi, Xin-Cheng Wen +4
Unsupervised reinforcement learning (RL) has emerged as a promising paradigm for enabling self-improvement in large language models (LLMs). However, existing unsupervised RL-based…
TCSR-SQL: Towards Table Content-aware Text-to-SQL with Self-retrieval
Wenbo Xu, Liang Yan, Chuanyi Liu +5
Large Language Model-based (LLM-based) Text-to-SQL methods have achieved important progress in generating SQL queries for real-world applications. When confronted with table conten…
SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning
Yuhao Zhang, Shaoming Duan, Jinhang Su +2
Despite the significant advancements of self-play fine-tuning (SPIN), which can transform a weak large language model (LLM) into a strong one through competitive interactions betwe…
CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Shaoming Duan, Zirui Wang, Chuanyi Liu +5
Recent advances in large language models (LLMs) have significantly improved the accuracy of Text-to-SQL systems. However, a critical challenge remains: the semantic mismatch betwee…