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SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression Nov 15, 2025

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SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training
Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Tasks
LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels

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LLM DataAug Hard Negative Sampling retrieval text-image retrieval dense retrieval haullcination LLM Agent contrastive learning dual encoder

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LLM DataAug Hard Negative Sampling retrieval text-image retrieval dense retrieval haullcination LLM Agent contrastive learning dual encoder

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