OpenAI Blog
Dec 15, 2022
New and improved embedding model
We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.
New embedding models and API updates
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OpenAI Blog
Jan 25, 2022
Introducing text and code embeddings
We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification.
arXiv AI
1d ago
Efficient Code Embeddings from Code Generation Models
arXiv:2508. 21290v2 Announce Type: replace-cross Abstract: jina-code-embeddings is a novel code embedding model suite designed to retrieve code from natural language queries, perform technical question-answering, and identify semantically similar code snippets across programming languages.
OpenAI Blog
Aug 6, 2024
Introducing Structured Outputs in the API
We are introducing Structured Outputs in the API—model outputs now reliably adhere to developer-supplied JSON Schemas.
arXiv AI
Jun 12
SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation
arXiv:2606. 13647v1 Announce Type: cross Abstract: We introduce SkMTEB, the first comprehensive MTEB-style text embedding benchmark for Slovak, a low-resource West Slavic language, comprising 31 datasets across 7 task types -- nearly 4$\times$ the depth of existing multilingual benchmark coverage for Slovak.
OpenAI Blog
Jun 11, 2020
OpenAI API
We’re releasing an API for accessing new AI models developed by OpenAI.
OpenAI Blog
Jun 13, 2023
Function calling and other API updates
We’re announcing updates including more steerable API models, function calling capabilities, longer context, and lower prices.
arXiv AI
1d ago
OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks
arXiv:2608. 15436v1 Announce Type: new Abstract: Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks.
arXiv AI
Jul 1
How Can AI Find My Model? A Model-Finding Experimental Study Considering Data Formats, Embeddings, and Retrieval Strategies
arXiv:2606. 30846v1 Announce Type: new Abstract: Discovering simulation models for reuse remains a fundamental challenge in Modeling and Simulation (M&S).
arXiv Machine Learning
Jul 28
TEmBed-T: A Multi-Dimensional Benchmark for Table-Level Embeddings
arXiv:2607. 24130v1 Announce Type: cross Abstract: Tabular data is the dominant structured-data modality, and learning table representations has become a core research direction.
arXiv AI
Jul 28
Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework
arXiv:2607. 23507v1 Announce Type: cross Abstract: Choosing the right text embedding model is one of the most consequential -- and most frequently under-examined -- decisions in building a retrieval or search system, yet the model that tops a leaderboard is rarely the best choice for a given deployment.
arXiv AI
Jun 3
ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models
arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.