microsoft/ms_marco
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Memory, Context & RAG
RAG architectures, hierarchical memory, semantic chunking, query rewriting, context compression for agents.
Note RAG benchmarking dataset
sentence-transformers/all-nli
Viewer • Updated • 2.86M • 4.29k • 47Note Embedding training data
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 14 -
Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models
Paper • 2402.04614 • Published • 3 -
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
Paper • 2404.16130 • Published • 7 -
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Paper • 2305.10601 • Published • 14 -
Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
Paper • 2409.04701 • Published • 1 -
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Paper • 2510.04618 • Published • 128 -
Compressing Context to Enhance Inference Efficiency of Large Language Models
Paper • 2310.06201 • Published -
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
Paper • 2310.05736 • Published • 4