2 citations · 3 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
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…