Tag
This paper introduces geometric stability measures—based on pairwise distance consistency in representations—to predict language model steerability and detect structural drift. Supervised variants achieve near-perfect correlation (ρ=0.89-0.97) with linear steerability across 35-69 embedding models, while unsupervised variants outperform CKA and Procrustes for post-deployment drift detection.
This article provides a technical guide on training and fine-tuning multimodal embedding and reranker models using the Sentence Transformers library, demonstrating performance improvements on Visual Document Retrieval tasks with Qwen3-VL.
Sentence Transformers v5.4 introduces support for multimodal embedding and reranking, allowing users to encode and compare text, images, audio, and video using a unified API.
This guide demonstrates how to fine-tune a domain-specific embedding model for RAG systems in under a day, covering data generation, hard negative mining, training, and deployment via NVIDIA NIM.
OpenAI released two new embedding models: text-embedding-3-small (5x cheaper than ada-002 with 40%+ MIRACL improvement) and text-embedding-3-large (best performance with up to 3072 dimensions). Both models show significant performance gains on standard benchmarks while reducing costs.