1 citations · 2 across the 14 of their papers we have counts for
14 papers
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…
Short Data, Long Context: Distilling Positional Knowledge in Transformers
Patrick Huber, Ernie Chang, Chinnadhurai Sankar +4
Extending the context window of language models typically requires expensive long-context pre-training, posing significant challenges for both training efficiency and data collecti…
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…
Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage
Siddhant Arora, Haidar Khan, Kai Sun +14
End-to-end speech-in speech-out dialogue systems are emerging as a powerful alternative to traditional ASR-LLM-TTS pipelines, generating more natural, expressive responses with sig…
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…
CoDi: Conversational Distillation for Grounded Question Answering
Patrick Huber, Arash Einolghozati, Rylan Conway +6
Distilling conversational skills into Small Language Models (SLMs) with approximately 1 billion parameters presents significant challenges. Firstly, SLMs have limited capacity in t…
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding
Trang Le, Daniel Lazar, Suyoun Kim +6
Spoken Language Understanding (SLU) is a critical component of voice assistants; it consists of converting speech to semantic parses for task execution. Previous works have explore…
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Charlie Hou, Akshat Shrivastava, Hongyuan Zhan +5
On-device training is currently the most common approach for training machine learning (ML) models on private, distributed user data. Despite this, on-device training has several d…
Large Language Models as Zero-shot Dialogue State Tracker through Function Calling
Zekun Li, Zhiyu Zoey Chen, Mike Ross +7
Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, thei…
Data-Efficiency with a Single GPU: An Exploration of Transfer Methods for Small Language Models
Alon Albalak, Akshat Shrivastava, Chinnadhurai Sankar +2
Multi-task learning (MTL), instruction tuning, and prompting have recently been shown to improve the generalizability of large language models to new tasks. However, the benefits o…
STOP: A dataset for Spoken Task Oriented Semantic Parsing
Paden Tomasello, Akshat Shrivastava, Daniel Lazar +12
End-to-end spoken language understanding (SLU) predicts intent directly from audio using a single model. It promises to improve the performance of assistant systems by leveraging a…
RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing
Vivek Gupta, Akshat Shrivastava, Adithya Sagar +2
While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory ha…
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks
Pranay Dighe, Saurabh Adya, Nuoyu Li +6
Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect o…
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant
Xi C. Chen, Adithya Sagar, Justine T. Kao +5
We describe a method for selecting relevant new training data for the LSTM-based domain selection component of our personal assistant system. Adding more annotated training data fo…