most citedApple Intelligence Foundation Language Models

4 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

cs.SD2026

StarTSE: Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model

Shuhai Peng, Hui Lu, Jinjiang Liu +8

While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in real-time applications. Direc…

cs.AI20264 cited

Apple Intelligence Foundation Language Models

Tom Gunter, Zirui Wang, Chong Wang +152

We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…

cs.CL2025

VoxRole: A Comprehensive Benchmark for Evaluating Speech-Based Role-Playing Agents

Weihao Wu, Liang Cao, Xinyu Wu +4

Recent significant advancements in Large Language Models (LLMs) have greatly propelled the development of Role-Playing Conversational Agents (RPCAs). These systems aim to create im…

cs.LG2025

Apple Intelligence Foundation Language Models: Tech Report 2025

Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395

We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…

cs.LG2025

Model Steering: Learning with a Reference Model Improves Generalization Bounds and Scaling Laws

Xiyuan Wei, Ming Lin, Fanjiang Ye +4

This paper formalizes an emerging learning paradigm that uses a trained model as a reference to guide and enhance the training of a target model through strategic data selection or…