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In this article, you will learn how to use probing classifiers, UMAP visualization, and SHAP values to interpret and analyze the quality of text embeddings generated by large language models.
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LLM inference in C/C++. Contribute to ggml-org/llama.cpp development by creating an account on GitHub.
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Google released Gemini 3.5 Transcribe, speech-to-text model reporting 2.6% word error rate across 85-plus languages, in public preview
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Abstract page for arXiv paper 2608.26363: A Unified Framework for the Mechanics of Information in Convolutional Neural Network Image Space
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Abstract page for arXiv paper 2608.26317: Modality Maturity Index: A benchmark for assessing multimodal capabilities of omni models
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Abstract page for arXiv paper 2608.26238: Procedura: Agentic 3D Modeling with Procedural Control
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Abstract page for arXiv paper 2608.26126: TelecomGPT-R1: A Unified Open-Source Reasoner for the Telecom Stack
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Abstract page for arXiv paper 2608.26125: Training-Time Explainability for Multilingual Hate Speech Detection: Aligning Model Reasoning with Human Rationales
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Abstract page for arXiv paper 2608.26124: Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework
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Abstract page for arXiv paper 2608.26121: Can a Model Catch Its Own Hallucinations for Free?: Label-Free Doubt Signals Hold Their Own Against a Labelled Dataset for Abstention
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Abstract page for arXiv paper 2608.26120: Recipes for Steering and Scaling LLMs via Sampling
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Abstract page for arXiv paper 2608.26118: ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements