Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in
  Smart Personal Assistant
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/.
681af7a58f41e99d8ca05074
Lei Shen
extracted_confirmed
2025-05-07T06:09:12.039Z
6454d2ee1a543cf97b1ba671
/avatars/30edc30cf3684bf685aabffcec8a3c30.svg
shen
user
lorashen
681af7a58f41e99d8ca05075
Xiaoyu Shen
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in
  Smart Personal Assistant
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/.
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.
large language models multi-agent frameworks benchmark datasets SLURP intelligent personal assistants language understanding task execution response generation