2 citations · 4 across the 8 of their papers we have counts for
8 papers
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…
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…
Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities
Yiming Huang, Jiyu Guo, Wenxin Mao +4
Converting natural language (NL) questions into SQL queries, referred to as Text-to-SQL, has emerged as a pivotal technology for facilitating access to relational databases, especi…
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…
QDA-SQL: Questions Enhanced Dialogue Augmentation for Multi-Turn Text-to-SQL
Yinggang Sun, Ziming Guo, Haining Yu +5
Fine-tuning large language models (LLMs) for specific domain tasks has achieved great success in Text-to-SQL tasks. However, these fine-tuned models often face challenges with mult…
ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization
Shuoran Jiang, Qingcai Chen, Youchen Pan +5
Lowering the memory requirement in full-parameter training on large models has become a hot research area. MeZO fine-tunes the large language models (LLMs) by just forward passes i…