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Dense X Retrieval: What Retrieval Granularity Should We Use?
Paper
• 2312.06648
• Published
• 1
Improving Text Embeddings with Large Language Models
Paper
• 2401.00368
• Published
• 82
Text Embeddings Reveal (Almost) As Much As Text
Paper
• 2310.06816
• Published
• 1
RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on
Agriculture
Paper
• 2401.08406
• Published
• 38
Multilingual E5 Text Embeddings: A Technical Report
Paper
• 2402.05672
• Published
• 22
Retrieval-Augmented Generation for Large Language Models: A Survey
Paper
• 2312.10997
• Published
• 12
Some Like It Small: Czech Semantic Embedding Models for Industry
Applications
Paper
• 2311.13921
• Published
Segment Any Text: A Universal Approach for Robust, Efficient and
Adaptable Sentence Segmentation
Paper
• 2406.16678
• Published
• 16