LLAMAPIE: Proactive In-Ear Conversation Assistants

TL;DR

LlamaPIE is a real-time, proactive in-ear conversation assistant that discreetly enhances human dialogue by predicting needs and providing concise guidance without interrupting, using on-device processing and a two-model pipeline.

Contribution

This work introduces LlamaPIE, the first proactive hearable assistant operating in real-time with on-device processing and a novel two-model approach for context-aware, unobtrusive assistance.

Findings

Effective in real-world datasets

User preference for proactive assistance

On-device processing enables real-time operation

Abstract

We introduce LlamaPIE, the first real-time proactive assistant designed to enhance human conversations through discreet, concise guidance delivered via hearable devices. Unlike traditional language models that require explicit user invocation, this assistant operates in the background, anticipating user needs without interrupting conversations. We address several challenges, including determining when to respond, crafting concise responses that enhance conversations, leveraging knowledge of the user for context-aware assistance, and real-time, on-device processing. To achieve this, we construct a semi-synthetic dialogue dataset and propose a two-model pipeline: a small model that decides when to respond and a larger model that generates the response. We evaluate our approach on real-world datasets, demonstrating its effectiveness in providing helpful, unobtrusive assistance. User studies with our assistant, implemented on Apple Silicon M2 hardware, show a strong preference for the proactive assistant over both a baseline with no assistance and a reactive model, highlighting the potential of LlamaPie to enhance live conversations.