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Anthropic/election_questions

published by @Anthropic

Alignment dataset tracked in the /datasets sub-catalog.

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Created on HF
2024-06-04
Age
2 years ago

Description

Election Evaluations Dataset

Dataset Summary

This dataset includes some of the evaluations we implemented to assess language models' ability to handle election-related information accurately, harmlessly, and without engaging in persuasion targeting.

Dataset Description

The dataset consists of three CSV files, each focusing on a specific aspect of election-related evaluations:

eu_accuracy_questions.csv:

Contains information-seeking questions about… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/election_questions.

Tags

language:enlicense:cc-by-nc-sa-4.0size_categories:n<1Kformat:csvmodality:textlibrary:datasetslibrary:dasklibrary:mlcroissantlibrary:polarsregion:us

README current version from Hugging Face


license: cc-by-nc-sa-4.0
language:

  • en
    size_categories:
  • 1K<n<10K

Election Evaluations Dataset

Dataset Summary

This dataset includes some of the evaluations we implemented to assess language models' ability to handle election-related information accurately, harmlessly, and without engaging in persuasion targeting.

Dataset Description

The dataset consists of three CSV files, each focusing on a specific aspect of election-related evaluations:

  1. eu_accuracy_questions.csv:

    • Contains information-seeking questions about European Union (EU) election-related topics.
    • The questions are designed to evaluate the accuracy of language models in providing factual information about EU elections.
  2. harmlessness_eval.csv:

    • Includes a mix of harmless and potentially harmful questions on a range of topics related to United States (US) elections.
    • Each question is labeled as either "Harmless" or "Harmful."
    • The goal is to assess whether language models can identify and refuse to answer questions that are labeled as "Harmful," while providing appropriate responses to "Harmless" questions.
  3. persuasion_targeting_eval.csv:

    • Contains questions related to various demographic groups within the United States in the context of elections.
    • Each question is labeled as either "Harmless" or "Harmful."
    • "Harmful" questions attempt to target specific populations for persuasion or manipulation.
    • The objective is to evaluate whether language models can identify and refuse to answer questions labeled as "Harmful", while providing appropriate responses to "Harmless" questions.

Disclaimer

Please note that these evaluations are generated by language models and may contain inaccuracies. While we conducted human evaluation on a subset of the dataset to assess quality, not all questions were manually reviewed. The dataset is intended for research purposes only and should not be considered a definitive assessment of language models' performance in handling election-related information.

Usage

from datasets import load_dataset
# Loading the data
dataset = load_dataset("Anthropic/election_questions")

Contact

For questions, you can email esin at anthropic dot com

README history 15 revisions

Every night we snapshot the README of every dataset in the catalog. When the SHA changes we archive the new version and diff it against the last. This is the evolving thought record of the alignment-data field: what the author decided to say about the corpus, and how that framing shifted over time.

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Used by abliterated models none yet

No abliterated models in our catalog have declared this dataset in their YAML metadata yet. This can mean the dataset is used but not declared, is used outside abliteration workflows, or is new. The link will populate automatically as models are indexed.

Community discussions 1 thread · 1 open · 1 comments

Threads posted by the community on Hugging Face: bug reports, questions about usage, pull requests on the README, license clarifications. Click any thread to expand and read the full conversation inline. Newest first.

  1. 2024-06-07[bot] Conversion to Parquetopen1 💬#1
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Read further

Metadata

License
cc-by-nc-sa-4.0

Repository

Download the dataset

0

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("Anthropic/election_questions")
Raw snapshot (Python)
from huggingface_hub import snapshot_download
snapshot_download(repo_id="Anthropic/election_questions", repo_type="dataset")
git clone (requires git-lfs)
git clone https://huggingface.co/datasets/Anthropic/election_questions
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