Dataset Card for "compred"

Dataset Details

Dataset Description

Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an average user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns about aggregating such diverse, often contradictory human feedback to train a single universal reward model, questioning which values or voices the models align with. Finetuning models to maximize such reward results in generic models that produce outputs not preferred by many user groups, as they tend to average out styles and norms. To study this issue, we collect and release ComPRed, a question-answering dataset with community-level preferences from Reddit. This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback.

• Curated by: Allen Institute for AI, The Ohio State University, Carnegie Mellon University
• Paper: ArXiv
• Repository: https://github.com/allenai/compred
• Language(s) (NLP): English
• License: https://opendatacommons.org/licenses/by/odc_by_1.0_public_text.txt
• Point of Contact: [Sachin Kumar & Chan Young Park](mailto:kumar.1145@osu.edu, chanyoun@andrew.cmu.edu)

Uses

ComPreD contains five subsets divided based on factors driving diverging user preferences (we followed a similar process as SHP to create this dataset).

Subset(s)	Factor
politics	Ideologies
gender_and_sexuality	Demographics
finance, history	Community Norms
science	Level of expertise / Community Norms

Loading

from datasets import load_dataset


# load finance train set
finance_train_pref = load_dataset("allenai/compred", "finance", split="train_pref")

# load finance test prompts
finance_test_prompts = load_dataset("allenai/compred", "finance_test_prompts", split="test_prompts")

Dataset Structure

Coming soon

Data Creation

Please refer to our paper for details on our dataset collection.

Licensing Information

ComPreD is made available under the ODC-BY requiring the user to follow the licenses of the subsequent parts.

Citation

@article{kumar-park2024,
  title={{Personalized LMs: Aligning Language Models with Diverse Human Preferences}},
  author={Sachin Kumar, Chan Young Park, Yulia Tsvetkov, Noah A. Smith, Hannaneh Hajishirzi},
  journal={},
  year={2024}
}