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Research questionHow can compact text embedding models improve retrieval and generalization through better training and data quality?Compact embedding models can underperform on retrieval and generalization when development emphasizes data scaling or synthesis without sufficiently addressing training methods and data quality. The problem is to improve their representations without relying on substantially larger models.
AI
Evaluation & Benchmarks
Information Retrieval
LLM Pretraining & Post-training
Machine Learning
Natural Language Processing
Research Paper
Small / On-device Models
Technology
Latest papersRecent research connected to this question, newest first.KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding ModelThe source studies 0.5-billion-parameter models that produce fixed-length embeddings through mean pooling and bidirectional representation learning. It reports a benchmark-based evaluation of models trained with staged weakly supervised, supervised, and contrastive objectives on data spanning many categories; the evidence concerns embedding performance and generalization rather than deployment behavior.research paper · Sep 3, 2026
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