Dataset Description, Collection, and Source

MCIF (Multimodal Crosslingual Instruction Following) is a multilingual human-annotated benchmark based on scientific talks that is designed to evaluate instruction-following in crosslingual, multimodal settings over both short- and long-form inputs. MCIF spans three core modalities -- speech, vision, and text -- and four diverse languages (English, German, Italian, and Chinese), enabling a comprehensive evaluation of MLLMs' abilities to interpret instructions across languages and combine them with multimodal contextual information.

License

CC-BY-4.0

Dataset Sources

Repository: MCIF
Paper: MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks

Dataset Structure

Data Config

There are 16 splits named using the pattern language_track_prompt_type, where:

language is one of en, de, it, or zh, indicating the output language.
track is either long or short, indicating the duration of the input.
prompt_type is either fixed or mixed, indicating whether the prompts are fixed or include paraphrases.

Dataset Fields

Field	Type	Description
`sample_id`	`int`	Unique identifier for the sample.
`audio_path`	`string`	File path to the input audio data.
`video_path`	`string`	File path to the input video data.
`instruction`	`string`	Task instruction associated with the sample.
`reference`	`string`	Reference text or ground-truth output.

In another two dedicated audio/video datasets:

Field	Type	Description
`audio_path` or `video_path`	`string`	File path to the input audio (.wav) or video (.mp4) data.
`file`	`Audio` / `Video`	Audio/video files.

Dataset Statistics

Citation

@misc{papi2025mcifmultimodalcrosslingualinstructionfollowing,
      title={MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks}, 
      author={Sara Papi and Maike Züfle and Marco Gaido and Beatrice Savoldi and Danni Liu and Ioannis Douros and Luisa Bentivogli and Jan Niehues},
      year={2025},
      eprint={2507.19634},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.19634}, 
}

Dataset Card Contact

@spapi