Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025MathematicsOpen Access

Enhanced Semantic Retrieval with Structured Prompt and Dimensionality Reduction for Big Data

View Full Paper
Ask AI
Bookmark
Share

Authors

DKDonghyeon KimMPMinki ParkJLJung Sun Lee

Discussion

Loading...

Member takes

Overview

Proposed structured RAG framework improves retrieval accuracy in big data, addressing LLM limitations.

Key Points

  • The proposed method improved clustering quality by 32.3%, enhancing decision-making in big data.
  • Using principal component analysis, the framework optimizes semantic retrieval and computational efficiency.
  • The study highlights the effectiveness of integrating multi-level filtering to eliminate redundancy.
  • This innovative approach addresses latency issues in traditional retrieval-augmented generation systems.

Cite This Study

Kim et al. (2025) studied this question.

synapsesocial.com/papers/68c1a90554b1d3bfb60e1ebahttps://doi.org/10.3390/math13152469
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Large Language Models are Zero-Shot Reasoners2022 · 1,120 citations
  2. 2MTEB: Massive Text Embedding Benchmark2023 · 421 citations
  3. 3Dual retrieving and ranking medical large language model with retrieval augmented generation2025 · 11 citations
  4. 4<scp>FinBERT</scp>: A Large Language Model for Extracting Information from Financial Text*2022 · 751 citations
  5. 5Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions2025 · 7 citations