6 papers
SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL
Geonho Lee, Min-Soo Kim
Large language models (LLMs) have advanced Text-to-SQL by enabling natural language interfaces to databases without task-specific fine-tuning. However, existing LLM-based systems r…
AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMS
Geonho Lee, Jeongho Park, Donghyoung Han +1
Recent Retrieval-Augmented Generation (RAG) systems increasingly combine vector retrieval with structured knowledge, such as Graph RAG and Filtered vector search. However, existing…
Value-and-Structure Alignment for Routing-Consistent Quantization of Mixture-of-Experts Models
Hancheol Park, Geonho Lee, Tairen Piao +1
Mixture-of-Experts (MoE) models scale foundation models efficiently by activating only a subset of experts for each token, but their large number of expert parameters still makes q…
ExtGraph: A Fast Extraction Method of User-intended Graphs from a Relational Database
Jeongho Park, Geonho Lee, Min-Soo Kim
Graph analytics is widely used in many fields to analyze various complex patterns. However, in most cases, important data in companies is stored in RDBMS's, and so, it is necessary…
MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAG
Taehwan Park, Geonho Lee, Min-Soo Kim
Retrieval-Augmented Generation (RAG) has proven effective on server infrastructures, but its application on mobile devices is still underexplored due to limited memory and power re…
RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy
Geonho Lee, Janghwan Lee, Sukjin Hong +4
Low-rank adaptation (LoRA) has become the dominant method for parameter-efficient LLM fine-tuning, with LoRA-based quantization error compensation (LQEC) emerging as a powerful too…