5 citations · 12 across the 6 of their papers we have counts for
5 papers · 1 filter
DB-Explore: Automated Database Exploration and Instruction Synthesis for Text-to-SQL
Haoyuan Ma, Yongliang Shen, Hengwei Liu +5
Recent text-to-SQL systems powered by large language models (LLMs) have demonstrated remarkable performance in translating natural language queries into SQL. However, these systems…
AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification
Xuan Zhang, Yongliang Shen, Zhe Zheng +6
Large language models (LLMs) have demonstrated remarkable capabilities in tool learning. In real-world scenarios, user queries are often ambiguous and incomplete, requiring effecti…
STaR-SQL: Self-Taught Reasoner for Text-to-SQL
Mingqian He, Yongliang Shen, Wenqi Zhang +3
Generating step-by-step "chain-of-thought" rationales has proven effective for improving the performance of large language models on complex reasoning tasks. However, applying such…
A Framework to Implement 1+N Multi-task Fine-tuning Pattern in LLMs Using the CGC-LORA Algorithm
Chao Song, Zhihao Ye, Qiqiang Lin +2
With the productive evolution of large language models (LLMs) in the field of natural language processing (NLP), tons of effort has been made to effectively fine-tune common pre-tr…
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
Wenqi Zhang, Yongliang Shen, Linjuan Wu +4
The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A post-hoc prompting strategy, e.g., reflexion and self-refine, refines LLM's response based…