pretty_name: QuRatedPajama-260B
QuRatedPajama
Paper: QuRating: Selecting High-Quality Data for Training Language Models
A 260B token subset of cerebras/SlimPajama-627B, annotated by princeton-nlp/QuRater-1.3B with sequence-level quality ratings across 4 criteria:
- Educational Value - e.g. the text includes clear explanations, step-by-step reasoning, or questions and answers
- Facts & Trivia - how much factual and trivia knowledge the text contains, where specific facts and obscure trivia are preferred over more common knowledge
- Writing Style - how polished and good is the writing style in the text
- Required Expertise: - how much required expertise and prerequisite knowledge is necessary to understand the text
In a pre-processing step, we split documents in into chunks of exactly 1024 tokens. We provide tokenization with the Llama-2 tokenizer in the input_ids column.
Guidance on Responsible Use
In the paper, we document various types of bias that are present in the quality ratings (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper). Hence, be aware that data selection with QuRating could have unintended and harmful effects on the language model that is being trained. We strongly recommend a comprehensive evaluation of the language model for these and other types of bias, particularly before real-world deployment. We hope that releasing the data/models can facilitate future research aimed at uncovering and mitigating such biases.
Citation:
@article{wettig2024qurating,
title={QuRating: Selecting High-Quality Data for Training Language Models},
author={Alexander Wettig, Aatmik Gupta, Saumya Malik, Danqi Chen},
journal={arXiv preprint 2402.09739},
year={2024}
}