87 citations · 141 across the 9 of their papers we have counts for
12 papers
When Interfaces Speak: Data-Aware Generative UI Harness for Active Interaction
Xiaolong Li, Xiaohan Xu, Jinyang Li +5
Most human-agent interaction today remains text-based. Natural language can impose cognitive overload, ambiguity, information chaos, and slow input for complex tasks; ephemeral gen…
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…
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…
Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents
Jinyang Li, Nan Huo, Yan Gao +7
Interactive Data Analysis, the collaboration between humans and LLM agents, enables real-time data exploration for informed decision-making. The challenges and costs of collecting…
Debiasing Recommendation with Personal Popularity
Wentao Ning, Reynold Cheng, Xiao Yan +4
Global popularity (GP) bias is the phenomenon that popular items are recommended much more frequently than they should be, which goes against the goal of providing personalized rec…
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Jinyang Li, Binyuan Hui, Ge Qu +15
Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT…
Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
Jinyang Li, Binyuan Hui, Reynold Cheng +7
The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years, as it can assis…
Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
Wentao Ning, Reynold Cheng, Jiajun Shen +7
Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs, meta-path-ba…