Research Highlights

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An LLM-Based Approach to Review Summarization on the App Store

April 24, 2025

Ratings and reviews are an invaluable resource for users exploring an app on the App Store, providing insights into how others have experienced the app. With review summaries now available in iOS 18.4, users can quickly get a high-level overview of what other users think about an app, while still having the option to dive into individual reviews for more detail. This feature is powered by a novel, multi-step LLM-based system that periodically…
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Apple Machine Learning Research at ICLR 2025

April 21, 2025research area Computer Vision, research area Methods and Algorithms, research area Speech and Natural Language Processingconference ICLR

Apple researchers are advancing machine learning (ML) and AI through fundamental research that improves the world’s understanding of this technology and helps to redefine what is possible with it. To support the broader research community and help accelerate progress in this field, we share much of our research through publications, open source resources, and engagement at conferences. This week, the Thirteenth International Conference on…
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Understanding Aggregate Trends for Apple Intelligence Using Differential Privacy

April 14, 2025research area Privacy

At Apple, we believe privacy is a fundamental human right. And we believe in giving our users a great experience while protecting their privacy. For years, we’ve used techniques like differential privacy as part of our opt-in device analytics program. This lets us gain insights into how our products are used, so we can improve them, while protecting user privacy by preventing Apple from seeing individual-level data from those users. This same…
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Controlling Language and Diffusion Models by Transporting Activations

April 10, 2025research area Computer Vision, research area Methods and Algorithms, research area Speech and Natural Language Processingconference ICLR

Large generative models are becoming increasingly capable and more widely deployed to power production applications, but getting these models to produce exactly what's desired can still be challenging. Fine-grained control over these models' outputs is important to meet user expectations and to mitigate potential misuses, ensuring the models' reliability and safety. To address these issues, Apple machine learning researchers have developed a new…
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Accelerating LLM Inference on NVIDIA GPUs with ReDrafter

December 18, 2024

Accelerating LLM inference is an important ML research problem, as auto-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency can reduce latency for users. In addition to ongoing efforts to accelerate inference on Apple silicon, we have recently made significant progress in accelerating LLM inference for the NVIDIA GPUs widely used for production applications across the industry. Earlier…
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Apple Machine Learning Research at NeurIPS 2024

December 6, 2024

Apple researchers are advancing the field of ML through fundamental research that improves the world’s understanding of this technology and helps to redefine what is possible with it. This work may lead to advancements in Apple's products and services, and the benefits of the research extend beyond the Apple ecosystem as it is shared with the broader research community through publication, open source resources, and engagement at industry and…
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On Device Llama 3.1 with Core ML

November 1, 2024

Many app developers are interested in building on device experiences that integrate increasingly capable large language models (LLMs). Running these models locally on Apple silicon enables developers to leverage the capabilities of the user's device for cost-effective inference, without sending data to and from third party servers, which also helps protect user privacy. In order to do this, the models must be carefully optimized to effectively…
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Combining Machine Learning and Homomorphic Encryption in the Apple Ecosystem

October 24, 2024research area Privacy

At Apple, we believe privacy is a fundamental human right. Our work to protect user privacy is informed by a set of privacy principles, and one of those principles is to prioritize using on-device processing. By performing computations locally on a user’s device, we help minimize the amount of data that is shared with Apple or other entities. Of course, a user may request on-device experiences powered by machine learning (ML) that can be enriched…
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Improving How Machine Translations Handle Grammatical Gender Ambiguity

October 7, 2024research area Speech and Natural Language Processing

Machine Translation (MT) enables people to connect with others and engage with content across language barriers. Grammatical gender presents a difficult challenge for these systems, as some languages require specificity for terms that can be ambiguous or neutral in other languages. For example, when translating the English word "nurse" into Spanish, one must decide whether the feminine "enfermera" or the masculine "enfermero" is appropriate…
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Introducing Apple’s On-Device and Server Foundation Models

June 10, 2024

At the 2024 Worldwide Developers Conference, we introduced Apple Intelligence, a personal intelligence system integrated deeply into iOS 18, iPadOS 18, and macOS Sequoia.

Apple Intelligence is comprised of multiple highly-capable generative models that are specialized for our users’ everyday tasks, and can adapt on the fly for their current activity. The foundation models built into Apple Intelligence have been fine-tuned for user experiences such as writing and refining text, prioritizing and summarizing notifications, creating playful images for conversations with family and friends, and taking in-app actions to simplify interactions across apps.

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