1 citations · 1 across the 6 of their papers we have counts for
6 papers
SkillMentor: LLM Agent Self-Evolution via Learning Blind-Spot Diagnosis
Xiaoyi Bao, Yuanzhen Xie, Yunzhi Tan +5
Agent self-evolution has primarily focused on learning how to act, while overlooking an equally important capability: learning to discover what an agent does not know. Existing app…
ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
Pei Guo, Enjie Liu, Yunzhi Tan +6
Table-based reasoning with large language models (LLMs), which requires reasoning based on natural language questions and structured tabular data, has gained widespread attention.…
DCMM-SQL: Automated Data-Centric Pipeline and Multi-Model Collaboration Training for Text-to-SQL Model
Yuanzhen Xie, Liu Ye, Jiqun Chu +5
Text-to-SQL tasks have gained attractive improvements since the release of ChatGPT. Among them, agent-based frameworks have been widely used in this field. However, the impact of d…
Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience
Jiawei Gu, Ziting Xian, Yuanzhen Xie +7
Large language models (LLMs) achieve strong performance on plain text tasks but underperform on structured data like tables and databases. Potential challenges arise from their und…
PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL
Zhuopan Yang, Yuanzhen Xie, Ruichao Zhong +6
It is challenging to convert natural language (NL) questions into executable structured query language (SQL) queries for text-to-SQL tasks due to the vast number of database schema…
Solid-SQL: Enhanced Schema-linking based In-context Learning for Robust Text-to-SQL
Geling Liu, Yunzhi Tan, Ruichao Zhong +5
Recently, large language models (LLMs) have significantly improved the performance of text-to-SQL systems. Nevertheless, many state-of-the-art (SOTA) approaches have overlooked the…