13 citations · 18 across the 14 of their papers we have counts for
15 papers
Audio LLMs Know When They Can't Hear You
Amirhosein Javadi, Richa Dixit, Mehrdad Farajtabar +3
Audio large language models allow users to interact with the model through speech. When an input recording is too degraded, the model may misinterpret the user's query and respond…
Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context
Keivan Alizadeh, Parshin Shojaee, Minsik Cho +1
Long-context handling remains a core challenge for language models: even with extended context windows, models often fail to reliably extract, reason over, and use the information…
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…
Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
Mohammad Samragh, Arnav Kundu, David Harrison +4
Autoregressive language models are constrained by their inherently sequential nature, generating one token at a time. This paradigm limits inference speed and parallelism, especial…
From Dense to Dynamic: Token-Difficulty Driven MoEfication of Pre-Trained LLMs
Kumari Nishu, Sachin Mehta, Samira Abnar +6
Training large language models (LLMs) for different inference constraints is computationally expensive, limiting control over efficiency-accuracy trade-offs. Moreover, once trained…
Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models
Keivan Alizadeh, Iman Mirzadeh, Hooman Shahrokhi +6
Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. To address this shortcoming, recen…
Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization
Mohammad Samragh, Iman Mirzadeh, Keivan Alizadeh Vahid +5
The pre-training phase of language models often begins with randomly initialized parameters. With the current trends in scaling models, training their large number of parameters ca…
SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting
Kumari Nishu, Minsik Cho, Devang Naik
User-defined keyword spotting on a resource-constrained edge device is challenging. However, keywords are often bounded by a maximum keyword length, which has been largely under-le…
LazyLLM: Dynamic Token Pruning for Efficient Long Context LLM Inference
Qichen Fu, Minsik Cho, Thomas Merth +3
The inference of transformer-based large language models consists of two sequential stages: 1) a prefilling stage to compute the KV cache of prompts and generate the first token, a…
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…
KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation
Minsik Cho, Mohammad Rastegari, Devang Naik
Large Language Model or LLM inference has two phases, the prompt (or prefill) phase to output the first token and the extension (or decoding) phase to the generate subsequent token…
LLM in a flash: Efficient Large Language Model Inference with Limited Memory
Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko +5
Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and me…
Streaming Anchor Loss: Augmenting Supervision with Temporal Significance
Utkarsh Oggy Sarawgi, John Berkowitz, Vineet Garg +5
Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the lear…
Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications
Duc N. M Hoang, Minsik Cho, Thomas Merth +2
Compressing Large Language Models (LLMs) often leads to reduced performance, especially for knowledge-intensive tasks. In this work, we dive into how compression damages LLMs' inhe…
eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models
Minsik Cho, Keivan A. Vahid, Qichen Fu +5
Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices fo…