Datasets:
File size: 2,394 Bytes
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license: mit
task_categories:
- question-answering
language:
- en
dataset_info:
features:
- name: id
dtype: string
- name: task_name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: question
dtype: string
- name: choice_a
dtype: string
- name: choice_b
dtype: string
- name: choice_c
dtype: string
- name: choice_d
dtype: string
- name: answer_gt
dtype: string
- name: category
dtype: string
- name: sub-category
dtype: string
- name: sub-sub-category
dtype: string
- name: linguistics_sub_discipline
dtype: string
splits:
- name: train
num_bytes: 1199569150.0
num_examples: 5000
download_size: 1466894219
dataset_size: 1199569150.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# [ICLR 2026] MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark
[](https://arxiv.org/pdf/2506.04779) [](https://github.com/dingdongwang/MMSU)
## Overview of MMSU
MMSU (Massive Multi-task Spoken Language Understanding and Reasoning Benchmark) is a comprehensive benchmark for evaluating fine-grained spoken language understanding and reasoning in multimodal models.
It systematically captures the variance of real-world linguistic phenomena in daily speech through **47 sub-tasks**, including phonetics, prosody, rhetoric, syntactics, semantics, and paralinguistics, spanning both perceptual and higher-level reasoning capabilities.
The benchmark comprises **5,000 carefully curated audio–question–answer pairs** derived from diverse authentic recordings.
## Usage
You can load the dataset via Hugging Face datasets:
```
from datasets import load_dataset
ds = load_dataset("ddwang2000/MMSU")
```
For evaluation, please refer to [**GitHub Code**](https://github.com/dingdongwang/MMSU)
## Citation
```
@article{wang2025mmsu,
title={MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark},
author={Dingdong Wang and Jincenzi Wu and Junan Li and Dongchao Yang and Xueyuan Chen and Tianhua Zhang and Helen Meng},
journal={arXiv preprint arXiv:2506.04779},
year={2025},
}
```
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