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Apple Researchers Propose MAD-Bench Benchmark to Overcome Hallucinations and Deceptive Prompts in Multimodal Large Language Models

AI NewsMarch 1, 2024159Views0Likes0Comments
Multimodal Large Language Models (MLLMs), having contributed to remarkable progress in AI, face challenges in accurately processing and responding to misleading information, leading to incorrect or hallucinated responses. This vulnerability raises concerns about the reliability of MLLMs in applications where accurate interpretation of text and visual data is crucial. Recent research has explored visual instruction…

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Improving LLM Inference Speeds on CPUs with Model Quantization | by Eduardo Alvarez | Feb, 2024

Data ScienceFebruary 29, 2024151Views0Likes0Comments
Image Property of Author — Create with NightcafeDiscover how to significantly improve inference latency on CPUs using quantization techniques for mixed, int8, and int4 precisions One of the most significant challenges the AI space faces is the need for computing resources to host large-scale production-grade LLM-based applications. At scale, LLM applications require redundancy, scalability, and…

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Streamlining Giants. The Evolution of Model Compression in… | by Nate Cibik | Feb, 2024

Data ScienceFebruary 29, 2024142Views0Likes0Comments
The quest to refine neural networks for practical applications traces its roots back to the foundational days of the field. When Rumelhart, Hinton, and Williams first demonstrated how to use the backpropagation algorithm to successfully train multi-layer neural networks that could learn complex, non-linear representations in 1986, the vast potential of these models became apparent.…

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