From the 2 of 6 linked papers with an AI index.
6 papers
SkillMentor: LLM Agent Self-Evolution via Learning Blind-Spot Diagnosis
Xiaoyi Bao, Yuanzhen Xie, Yunzhi Tan +5
The paper presents SkillMentor, a reinforcement‑learning trained mentor that enables large language model agents to learn how to identify and diagnose their own blind‑spot failures…
ProgramTab: Boosting Table Reasoning of LLMs via Programmatic Paradigm
Pei Guo, Enjie Liu, Yunzhi Tan +6
The paper introduces ProgramTab, a framework that uses in‑context learning and Python code generation to preprocess large tables and extract relevant sub‑tables, enabling large lan…
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…