107 citations · 129 across the 20 of their papers we have counts for
22 papers
Selm: Self-Evolving Library Management for Multi-Skill Agent Optimization
Mengxi Hu, Yuxuan Hu, Jinbo Su +6
Large language model (LLM) agents increasingly use skills as a lightweight way to acquire task-specific knowledge. Recent work enables skills to evolve from task trajectories. Howe…
ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models
Xinmei Huang, Jie Song, Peng Li +10
Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines. Existing S…
IDSTune: A Multi-Agent Collaborative Framework for Integrated Database System Tuning
Yiyan Li, Guanli Liu, Renata Borovica-Gajic +6
Database tuning is critical for achieving high performance in modern database management systems (DBMSs). Existing methods typically optimize a single component---knobs, indexes, o…
TableCache: Primary Foreign Key Guided KV Cache Precomputation for Low Latency Text-to-SQL
Jinbo Su, Yuxuan Hu, Cuiping Li +4
In Text-to-SQL tasks, existing LLM-based methods often include extensive database schemas in prompts, leading to long context lengths and increased prefilling latency. While user q…
QUAD: Quantization and Parameter-Efficient Tuning of LLM with Activation Decomposition
Yuxuan Hu, Xiaodong Chen, Cuiping Li +2
Large Language Models (LLMs) excel in diverse applications but suffer inefficiency due to massive scale. While quantization reduces computational costs, existing methods degrade ac…
A Query Optimization Method Utilizing Large Language Models
Zhiming Yao, Haoyang Li, Jing Zhang +2
Query optimization is a critical task in database systems, focused on determining the most efficient way to execute a query from an enormous set of possible strategies. Traditional…
LLMIA: An Out-of-the-Box Index Advisor via In-Context Learning with LLMs
Xinxin Zhao, Xinmei Huang, Haoyang Li +7
Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimat…
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
Haoyang Li, Shang Wu, Xiaokang Zhang +9
Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements…
P Law: Scaling Law for Post-Training After Model Pruning
Xiaodong Chen, Yuxuan Hu, Xiaokang Zhang +4
Pruning has become a widely adopted technique for reducing the hardware requirements of large language models (LLMs). To recover model performance after pruning, post-training is c…
PCQPR: Proactive Conversational Question Planning with Reflection
Shasha Guo, Lizi Liao, Jing Zhang +2
Conversational Question Generation (CQG) enhances the interactivity of conversational question-answering systems in fields such as education, customer service, and entertainment. H…
Is Large Language Model Good at Database Knob Tuning? A Comprehensive Experimental Evaluation
Yiyan Li, Haoyang Li, Zhao Pu +6
Knob tuning plays a crucial role in optimizing databases by adjusting knobs to enhance database performance. However, traditional tuning methods often follow a Try-Collect-Adjust a…
A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering
Lingxi Zhang, Jing Zhang, Yanling Wang +2
Large-scale knowledge bases (KBs) like Freebase and Wikidata house millions of structured knowledge. Knowledge Base Question Answering (KBQA) provides a user-friendly way to access…
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Shasha Guo, Lizi Liao, Jing Zhang +3
Knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the…
E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model
Xinmei Huang, Haoyang Li, Jing Zhang +7
Database knob tuning is a significant challenge for database administrators, as it involves tuning a large number of configuration knobs with continuous or discrete values to achie…
Open-World Semi-Supervised Learning for Node Classification
Yanling Wang, Jing Zhang, Lingxi Zhang +5
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-…
Streamlining Redundant Layers to Compress Large Language Models
Xiaodong Chen, Yuxuan Hu, Jing Zhang +3
This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on…
CodeS: Towards Building Open-source Language Models for Text-to-SQL
Haoyang Li, Jing Zhang, Hanbing Liu +7
Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA)…
Diversifying Question Generation over Knowledge Base via External Natural Questions
Shasha Guo, Jing Zhang, Xirui Ke +2
Previous methods on knowledge base question generation (KBQG) primarily focus on enhancing the quality of a single generated question. Recognizing the remarkable paraphrasing abili…
: Enhancing Structured Pruning via PCA Projection
Yuxuan Hu, Jing Zhang, Zhe Zhao +4
Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidde…
FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering
Lingxi Zhang, Jing Zhang, Yanling Wang +5
The generalization problem on KBQA has drawn considerable attention. Existing research suffers from the generalization issue brought by the entanglement in the coarse-grained model…
RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
Haoyang Li, Jing Zhang, Cuiping Li +1
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of p…
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Jing Zhang, Xiaokang Zhang, Jifan Yu +4
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning. A desired subgraph is crucial as a small one may exclude the answer but a large on…