1 citations · 2 across the 7 of their papers we have counts for
10 papers
Diff-SQL: SQL Efficiency Optimization via Patch Generation and Constraint Alignment
Shipei Lin, Duomin Zhang, Xiaolong Li +4
SQL efficiency optimization aims to transform slow queries into semantically equivalent but faster alternatives. However, directly optimizing SQL with large language models in an e…
ORCA: Evaluating LLMs on Data Science Code Translation
Xiaolong Li, Jinyang Li, Bowen Qin +5
Data Science Code Translation (DSCT) is the process of converting code between data science libraries while preserving functional equivalence and enabling interoperability across d…
Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents
Bowen Qin, Yi Xie
Modern coding agents are usually evaluated by whether they eventually produce a correct patch, but patch generation depends on an earlier context-acquisition stage: finding the rep…
Hint Tuning: Less Data Makes Better Reasoners
Siqi Fan, Minghao Li, Xiaoqian Ma +6
Large reasoning models achieve high accuracy through extended chain-of-thought but generate 5--8 more tokens than necessary, applying verbose reasoning uniformly regardless of prob…
LogDx-CI: Benchmarking Log Reduction Tools for LLM Root-Cause Diagnosis
Bowen Qin
CI failure logs are large (median 5k lines, max 200k in this corpus) and noisy. Coding agents that try to debug them depend on an upstream tool to reduce the log to a manageable co…
BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions
Nan Huo, Xiaohan Xu, Jinyang Li +21
Large language models (LLMs) have demonstrated remarkable performance on single-turn text-to-SQL tasks, but real-world database applications predominantly require multi-turn intera…
SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
Jinyang Li, Xiaolong Li, Ge Qu +17
Resolution of complex SQL issues persists as a significant bottleneck in real-world database applications. Current Large Language Models (LLMs), while adept at text-to-SQL translat…
Micro-Act: Mitigating Knowledge Conflict in LLM-based RAG via Actionable Self-Reasoning
Nan Huo, Jinyang Li, Bowen Qin +5
Retrieval-Augmented Generation (RAG) systems commonly suffer from Knowledge Conflicts, where retrieved external knowledge contradicts the inherent, parametric knowledge of large la…
SHARE: An SLM-based Hierarchical Action CorREction Assistant for Text-to-SQL
Ge Qu, Jinyang Li, Bowen Qin +4
Current self-correction approaches in text-to-SQL face two critical limitations: 1) Conventional self-correction methods rely on recursive self-calls of LLMs, resulting in multipli…
Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation
Ge Qu, Jinyang Li, Bowen Li +4
Large Language Models (LLMs) driven by In-Context Learning (ICL) have significantly improved the performance of text-to-SQL. Previous methods generally employ a two-stage reasoning…