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Tuesday, 5 March 2024

Do text embeddings perfectly encode text?

The Gradient 2 years ago 42

Researchers demonstrated that text can be recovered from embedding vectors used in RAG systems and vector databases by training a model called vec2text that iteratively generates text hypotheses to match target embeddings. The method achieved 92% exact match recovery on 32-token sequences with 50 optimization steps and a BLEU score of 97. This raises security concerns for systems storing embeddings of sensitive documents, prompting future work on building embedding models that resist inversion while remaining useful.

OpenAI and Elon Musk

OpenAI 2 years ago 38

I'd be happy to help, but the article you've provided contains only a single sentence that doesn't convey substantive news content. There's no information about what happened, specific details, or consequences to summarize. Could you provide the full article text?

Introducing ConTextual: How well can your Multimodal model jointly reason over text and image in text-rich scenes?

Hugging Face 2 years ago 18

Researchers at UCLA created ConTextual, a benchmark dataset with 506 instructions designed to evaluate how well multimodal AI models can reason jointly about text and images in text-rich scenes like maps, shopping interfaces, and infographics. The dataset covers 8 real-world visual scenarios, and initial experiments tested 13 models including GPT-4V, Gemini Vision Pro, and open-source alternatives like LLaVA-1.5-13B. Current models substantially underperform humans on the benchmark, with even the best proprietary model GPT-4V struggling on time-reading and infographic tasks, suggesting the need for better image encoders and vision-language alignment techniques.

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