MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval Paper • 2412.14475 • Published 8 days ago • 51
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery Paper • 2409.05591 • Published Sep 9 • 29 • 4
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery Paper • 2409.05591 • Published Sep 9 • 29
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery Paper • 2409.05591 • Published Sep 9 • 29
MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery Paper • 2409.05591 • Published Sep 9 • 29 • 4
RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder Paper • 2205.12035 • Published May 24, 2022
C-Pack: Packaged Resources To Advance General Chinese Embedding Paper • 2309.07597 • Published Sep 14, 2023 • 1
Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings Paper • 2204.00185 • Published Apr 1, 2022
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval Paper • 2201.05409 • Published Jan 14, 2022
Matching-oriented Product Quantization For Ad-hoc Retrieval Paper • 2104.07858 • Published Apr 16, 2021
LM-Cocktail: Resilient Tuning of Language Models via Model Merging Paper • 2311.13534 • Published Nov 22, 2023 • 4
Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon Paper • 2401.03462 • Published Jan 7 • 27
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning Paper • 2401.06532 • Published Jan 12 • 12
Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization Paper • 2401.07793 • Published Jan 15 • 3
Making Large Language Models A Better Foundation For Dense Retrieval Paper • 2312.15503 • Published Dec 24, 2023