title: Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in
    Smart Personal Assistant
summary: In recent years, multi-agent frameworks powered by large language models
  (LLMs) have advanced rapidly. Despite this progress, there is still a notable
  absence of benchmark datasets specifically tailored to evaluate their
  performance. To bridge this gap, we introduce Auto-SLURP, a benchmark dataset
  aimed at evaluating LLM-based multi-agent frameworks in the context of
  intelligent personal assistants. Auto-SLURP extends the original SLURP dataset
  -- initially developed for natural language understanding tasks -- by
  relabeling the data and integrating simulated servers and external services.
  This enhancement enables a comprehensive end-to-end evaluation pipeline,
  covering language understanding, task execution, and response generation. Our
  experiments demonstrate that Auto-SLURP presents a significant challenge for
  current state-of-the-art frameworks, highlighting that truly reliable and
  intelligent multi-agent personal assistants remain a work in progress. The
  dataset and related code are available at
  https://github.com/lorashen/Auto-SLURP/.
paper_authors._id: 681af7a58f41e99d8ca05074
paper_authors.name: Lei Shen
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-07T06:09:12.039Z
paper_authors._id: 6454d2ee1a543cf97b1ba671
paper_authors.avatarUrl: /avatars/30edc30cf3684bf685aabffcec8a3c30.svg
paper_authors.fullname: shen
paper_authors.type: user
paper_authors.user: lorashen
paper_authors._id: 681af7a58f41e99d8ca05075
paper_authors.name: Xiaoyu Shen
paper_title: Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in
    Smart Personal Assistant
paper_summary: In recent years, multi-agent frameworks powered by large language models
  (LLMs) have advanced rapidly. Despite this progress, there is still a notable
  absence of benchmark datasets specifically tailored to evaluate their
  performance. To bridge this gap, we introduce Auto-SLURP, a benchmark dataset
  aimed at evaluating LLM-based multi-agent frameworks in the context of
  intelligent personal assistants. Auto-SLURP extends the original SLURP dataset
  -- initially developed for natural language understanding tasks -- by
  relabeling the data and integrating simulated servers and external services.
  This enhancement enables a comprehensive end-to-end evaluation pipeline,
  covering language understanding, task execution, and response generation. Our
  experiments demonstrate that Auto-SLURP presents a significant challenge for
  current state-of-the-art frameworks, highlighting that truly reliable and
  intelligent multi-agent personal assistants remain a work in progress. The
  dataset and related code are available at
  https://github.com/lorashen/Auto-SLURP/.
paper_ai_summary: Auto-SLURP extends SLURP for evaluating LLM-based multi-agent intelligent personal assistants, offering a comprehensive benchmark for language understanding, task execution, and response generation.
paper_ai_keywords: large language models multi-agent frameworks benchmark datasets SLURP intelligent personal assistants language understanding task execution response generation