title: LLAMAPIE: Proactive In-Ear Conversation Assistants
summary: 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.
paper_authors._id: 68239705889f2a9d374c1774
paper_authors.name: Tuochao Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T12:57:14.833Z
paper_authors._id: 65a9d16f3b9e1f0f307c96c3
paper_authors.avatarUrl: /avatars/882a0746a0adb2d7befe563f7fbc6321.svg
paper_authors.fullname: Tuochao Chen
paper_authors.type: user
paper_authors.user: tuochao
paper_authors._id: 68239705889f2a9d374c1775
paper_authors.name: Nicholas Batchelder
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-14T12:57:20.805Z
paper_authors._id: 64de5c6892f3bd97865b0e1e
paper_authors.avatarUrl: /avatars/3141199aa7e1d345ec647af749eeff37.svg
paper_authors.fullname: Nicholas Batchelder
paper_authors.type: user
paper_authors.user: nicbat
paper_authors._id: 68239705889f2a9d374c1776
paper_authors.name: Alisa Liu
paper_authors._id: 68239705889f2a9d374c1777
paper_authors.name: Noah Smith
paper_authors._id: 68239705889f2a9d374c1778
paper_authors.name: Shyamnath Gollakota
paper_title: LLAMAPIE: Proactive In-Ear Conversation Assistants
paper_summary: 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.
paper_ai_summary: LlamaPIE, a real-time proactive assistant for conversations, uses a two-model pipeline with a semi-synthetic dialogue dataset to provide unobtrusive guidance via hearable devices.
paper_ai_keywords: hearable devices dialogue dataset two-model pipeline