Pith. sign in

REVIEW 1 cited by

Intelligent Assistant Language Understanding On Device

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2308.03905 v1 pith:EWL4NCPW submitted 2023-08-07 cs.CL cs.AIcs.LG

Cecilia Aas , Hisham Abdelsalam , Irina Belousova , Shruti Bhargava , Jianpeng Cheng , Robert Daland , Joris Driesen , Federico Flego
show 15 more authors
Tristan Guigue Anders Johannsen Partha Lal Jiarui Lu Joel Ruben Antony Moniz Nathan Perkins Dhivya Piraviperumal Stephen Pulman Diarmuid Ó Séaghdha David Q. Sun John Torr Marco Del Vecchio Jay Wacker Jason D. Williams Hong Yu
This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords assistantdescribedevicelanguagepersonalsystemunderstandingaccurate
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding system that runs on device. In comparison to a server-based assistant, this system is more private, more reliable, faster, more expressive, and more accurate. We describe what led to key choices about architecture and technologies. For example, some approaches in the dialog systems literature are difficult to maintain over time in a deployment setting. We hope that sharing learnings from our practical experiences may help inform future work in the research community.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UnionSparse: An Index-Efficient Sparsity Framework for Low-Bit Sparse LLM Inference on Edge

    cs.DC 2026-08 conditional novelty 6.0 of 10

    UnionSparse combines a shared-bitmap sparse format with a Tensor Core SpMM kernel to speed up low-bit sparse LLM decoding on edge GPUs by cutting index traffic.

Pith tools