200 citations · 233 across the 10 of their papers we have counts for
6 papers · 1 filter
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment
Hanxian Huang, Igor Fedorov, Andrey Gromov +14
Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce nea…
MobileLLM-Pro Technical Report
Patrick Huber, Ernie Chang, Wei Wen +16
Efficient on-device language models around 1 billion parameters are essential for powering low-latency AI applications on mobile and wearable devices. However, achieving strong per…
CoSMoEs: Compact Sparse Mixture of Experts
Patrick Huber, Akshat Shrivastava, Ernie Chang +3
Sparse Mixture of Expert (MoE) models are popular foundational architectures at large scale, however, under-explored at smaller sizes. Here, we show how to enable Compact Sparse Mi…
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
Arvind Neelakantan, Semih Yavuz, Sharan Narang +5
Task-oriented dialog presents a difficult challenge encompassing multiple problems including multi-turn language understanding and generation, knowledge retrieval and reasoning, an…
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes
Chinnadhurai Sankar, Sujith Ravi
Open domain dialog systems face the challenge of being repetitive and producing generic responses. In this paper, we demonstrate that by conditioning the response generation on int…
The Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Iulian Vlad Serban, Chinnadhurai Sankar, Michael Pieper +2
Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-eff…