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aisingapore/Instruction-Following-IFEval

published by @aisingapore

Alignment dataset tracked in the /datasets sub-catalog.

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Size
377 KB
Created on HF
2024-12-11
Age
1 years ago

Description

SEA-IFEval

SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. It is based on IFEval and was manually translated by native speakers for Indonesian, Javanese, Sundanese, Thai, Tagalog, and Vietnamese.

Supported Tasks and Leaderboards

SEA-IFEval is designed for evaluating chat or instruction-tuned large language models… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/Instruction-Following-IFEval.

Tags

task_categories:text-generationlanguage:idlanguage:jvlanguage:sulanguage:thlanguage:tllanguage:vilicense:cc-by-4.0size_categories:1K<n<10Kformat:parquetformat:optimized-parquetmodality:textlibrary:datasetslibrary:pandaslibrary:polarslibrary:mlcroissantarxiv:2311.07911region:us

README current version from Hugging Face

pretty_name: SEA-IFEval
license:

  • cc-by-4.0
    task_categories:
  • text-generation
    language:
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  • jv
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SEA-IFEval

SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. It is based on IFEval and was manually translated by native speakers for Indonesian, Javanese, Sundanese, Thai, Tagalog, and Vietnamese.

Supported Tasks and Leaderboards

SEA-IFEval is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the SEA-HELM leaderboard from AI Singapore.

Languages

  • Indonesian (id)
  • Javanese (jv)
  • Sundanese (su)
  • Tagalog (tl)
  • Thai (th)
  • Vietnamese (vi)

Dataset Details

SEA-IFEval is split by language. Below are the statistics for this dataset. The number of tokens only refer to the strings of text found within the prompts column.

Split # of examples # of GPT-4o tokens # of Gemma 2 tokens # of Llama 3 tokens
en 105 3545 3733 3688
id 105 4512 4146 5444
jv 105 4409 4901 5654
su 105 4762 5651 6525
th 105 5905 5472 7035
tl 105 5525 5987 6736
vi 105 5217 5069 5171
total 735 33875 34959 40253

Data Sources

Data Source License Language/s Split/s
IFEval Apache 2.0 English en
SEA-IFEval^ CC BY 4.0 Indonesian, Javanese, Tagalog, Sundanese, Vietnamese id, jv, tl, su, vi

^ manually translated from IFEval

License

For the license/s of the dataset/s, please refer to the data sources table above.

We endeavor to ensure data used is permissible and have chosen datasets from creators who have processes to exclude copyrighted or disputed data.

Acknowledgement

This project is supported by the National Research Foundation Singapore and Infocomm Media Development Authority (IMDA),
Singapore under its National Large Language Model Funding Initiative.

References

@misc{zhou2023instructionfollowingevaluationlargelanguage,
      title={Instruction-Following Evaluation for Large Language Models}, 
      author={Jeffrey Zhou and Tianjian Lu and Swaroop Mishra and Siddhartha Brahma and Sujoy Basu and Yi Luan and Denny Zhou and Le Hou},
      year={2023},
      eprint={2311.07911},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2311.07911}, 
}

README history 6 revisions

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Community discussions 2 threads · 1 open · 2 comments

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The Korean scepticism about one-click tools and non-English benchmark blindspots.

Metadata

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cc-by-4.0

Repository

DIR en DIR id DIR jv DIR km DIR lo DIR ms DIR my DIR su DIR ta DIR th DIR tl DIR vi FILE .gitattributes FILE README.md

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Download the dataset

Gated dataset. You need to accept the author's terms on Hugging Face before downloading. Visit the repository, click Agree and access repository, then log in locally with huggingface-cli login and use one of the snippets below.

Copy any of these snippets into your notebook or terminal. All three fetch directly from Hugging Face using your own credentials.

Recommended: datasets library (Python) 
from datasets import load_dataset
ds = load_dataset("aisingapore/Instruction-Following-IFEval")
Raw snapshot (Python) 
from huggingface_hub import snapshot_download
snapshot_download(repo_id="aisingapore/Instruction-Following-IFEval", repo_type="dataset")
git clone (requires git-lfs) 
git clone https://huggingface.co/datasets/aisingapore/Instruction-Following-IFEval
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