107 citations · 183 across the 26 of their papers we have counts for
27 papers
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…
LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model
Yuxuan Hu, Jing Zhang, Xiaodong Chen +3
Existing low-rank adaptation (LoRA) methods face challenges on sparse large language models (LLMs) due to the inability to maintain sparsity. Recent works introduced methods that m…
SAM Decoding: Speculative Decoding via Suffix Automaton
Yuxuan Hu, Ke Wang, Xiaokang Zhang +4
Speculative decoding (SD) has been demonstrated as an effective technique for lossless LLM inference acceleration. Retrieval-based SD methods, one kind of model-free method, have y…
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)…
Large Language Model for Table Processing: A Survey
Weizheng Lu, Jing Zhang, Ju Fan +3
Tables, typically two-dimensional and structured to store large amounts of data, are essential in daily activities like database queries, spreadsheet manipulations, web table quest…
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…
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models
Yu Feng, Jing Zhang, Xiaokang Zhang +3
Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constra…
A Multi-Strategy based Pre-Training Method for Cold-Start Recommendation
Bowen Hao, Hongzhi Yin, Jing Zhang +2
Cold-start problem is a fundamental challenge for recommendation tasks. The recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model, PT-GNN, pre-trains the GNN…
Recommending Courses in MOOCs for Jobs: An Auto Weak Supervision Approach
Bowen Hao, Jing Zhang, Cuiping Li +2
The proliferation of massive open online courses (MOOCs) demands an effective way of course recommendation for jobs posted in recruitment websites, especially for the people who ta…