Posts in category: Apple

Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts. Their inference cost scales quadratically with respect to sequence length, making it expensive for deployment in some real-world text processing applications, such as retrieval-augmented generation (RAG). Additionally, LLMs also exhibit the “distraction phenomenon,” where irrelevant context in the prompt degrades output quality. To address these drawbacks, we propose a novel RAG prompting methodology, superposition prompting, which can be directly applied to…Apple Machine Learning Research

Consider the supervised learning setting where the goal is to learn to predict labels y given points x from a distribution. An omnipredictor for a class L of loss functions and a class C of hypotheses is a predictor whose predictions incur less expected loss than the best hypothesis in C for every loss in L. Since the work of [GKR+21] that introduced the notion, there has been a large body of work in the setting of binary labels where y∈{0,1}, but much less is known about the regression setting where y∈[0,1] can be continuous. Our main conceptual contribution is the notion of sufficient…Apple Machine Learning Research

Consider a multi-class labelling problem, where the labels can take values in [k], and a predictor predicts a distribution over the labels. In this work, we study the following foundational question: Are there notions of multi-class calibration that give strong guarantees of meaningful predictions and can be achieved in time and sample complexities polynomial in k? Prior notions of calibration exhibit a tradeoff between computational efficiency and expressivity: they either suffer from having sample complexity exponential in k, or needing to solve computationally intractable problems, or give…Apple Machine Learning Research

In recent years, the evolution of end-to-end (E2E) automatic speech recognition (ASR) models has been remarkable, largely due to advances in deep learning architectures like transformer. On top of E2E systems, researchers have achieved substantial accuracy improvement by rescoring E2E model’s N-best hypotheses with a phoneme-based model. This raises an interesting question about where the improvements come from other than the system combination effect. We examine the underlying mechanisms driving these gains and propose an efficient joint training approach, where E2E models are trained jointly…Apple Machine Learning Research

This paper was accepted at the Efficient Natural Language and Speech Processing (ENLSP-III) Workshop at NeurIPS.
Large, pre-trained models are problematic to use in resource constrained applications. Fortunately, task-aware structured pruning methods offer a solution. These approaches reduce model size by dropping structural units like layers and attention heads in a manner that takes into account the end-task. However, these pruning algorithms require more task-specific data than is typically available. We propose a framework which combines structured pruning with transfer learning to reduce…Apple Machine Learning Research

We propose utilizing n-best reranking to enhance Sequence-Level Knowledge Distillation (Kim and Rush, 2016) where we extract pseudo-labels for student model’s training data from top n-best hypotheses and leverage a diverse set of models with different inductive biases, objective functions or architectures, including some publicly-available large language models, to pick the highest-quality hypotheses as labels. The effectiveness of our proposal is validated through experiments on the WMT’21 German ↔ English and Chinese ↔ English translation tasks. Our results demonstrate that utilizing…Apple Machine Learning Research

Modern deep learning approaches usually utilize modality-specific processing. For example, the most common deep learning approach to image classification involves decoding image file bytes into an RGB tensor which is passed into a neural network. Instead, we investigate modality-independent representation learning by performing classification directly on file bytes, without the need for decoding files at inference time. This enables models to operate on various modalities without any hand-designed, modality-specific processing. Our model, ByteFormer, improves ImageNet Top-1 classification…Apple Machine Learning Research

We study the problem of private vector mean estimation in the shuffle model of privacy where nnn users each have a unit vector in ddd dimensions. We propose a new multi-message protocol that achieves the optimal error using O~(min⁡(nε2,d))tilde{mathcal{O}}left(min(nvarepsilon^2,d)right)O~(min(nε2,d)) messages per user. Moreover, we show that any (unbiased) protocol that achieves optimal error requires each user to send Ω(min⁡(nε2,d)/log⁡(n))Omega(min(nvarepsilon^2,d)/log(n))Ω(min(nε2,d)/log(n)) messages, demonstrating the optimality of our message complexity up to logarithmic…Apple Machine Learning Research

We introduce MIA-Bench, a new benchmark designed to evaluate multimodal large language models (MLLMs) on their ability to strictly adhere to complex instructions. Our benchmark comprises a diverse set of 400 image-prompt pairs, each crafted to challenge the models’ compliance with layered instructions in generating accurate responses that satisfy specific requested patterns. Evaluation results from a wide array of state-of-the-art MLLMs reveal significant variations in performance, highlighting areas for improvement in instruction fidelity. Additionally, we create extra training data and…Apple Machine Learning Research

Apple Machine Learning Research