LLAMAPIE: Proactive In-Ear Conversation Assistants
Authors: name: Tuochao Chen, name: Nicholas Batchelder, name: Alisa Liu, name: Noah Smith, name: Shyamnath Gollakota

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.