Tag: #embeddings36 results
Google unveils its first native multimodal embedding model, unifying text, images, videos, and audio into a single embedding space. Dramatic demos show even lobsters 'understanding' screens. This advances cross-modal AI understanding.
Google has launched the Gemini Embedding 2 model, a new multimodal embedding solution. It supports text, image, video, audio, and document embeddings in a unified vector space. The model is accessible via the Gemini API and Vertex AI, with customizable output dimensions.
Perplexity has launched two new embedding models: pplx-embed-v1 and pplx-embed-context-v1, optimized for large-scale retrieval tasks. These models incorporate int8 and binary quantization to enhance efficiency. They are available in 0.6B and 4B parameter sizes.
Researchers propose a geometric taxonomy classifying LLM hallucinations into three types: unfaithfulness, confabulation, and factual error. Benchmark hallucinations show strong domain-local detection but fail cross-domain, while human-crafted confabulations enable a single global detection direction. Factual errors remain undetectable via embeddings due to distributional encoding limits.
This interactive tool visualizes 32,070 alphabetic tokens from GPT-2-small's embedding space. Users can explore nearest-neighbor relationships and navigate the graph using a t-SNE layout.
This research explores how GPT-2 Small represents the token 'Trump' in its static embedding space. It compares discretized versus continuous coordinate representations to reveal how different mathematical treatments affect the semantic clustering of related tokens.
Meta is researching a new upstream representation layer to better connect user interests with advertiser offerings. This approach aims to improve deep funnel ad performance by learning unified embeddings across the entire ecosystem.
A developer reports that DINOv2 underperforms significantly compared to SigLIP2 in a fine-grained car classification task using k-NN. The discussion explores whether DINOv2's self-supervised nature requires a trained head rather than raw embedding retrieval.
This study explores how AI and retrieval-based strategies can improve the discovery and reuse of simulation models. It evaluates the impact of data representation, embedding models, and reranking techniques on retrieval performance.
The Global Research Space is a new platform that visualizes 11 million scientific papers from OpenAlex and Arxiv. It uses SPECTER 2 embeddings and UMAP dimensionality reduction to allow users to explore research trends through an interactive, time-aware map.