LongWanjuan: Towards Systematic Measurement for Long Text Quality
<|reference_start|>LongWanjuan: Towards Systematic Measurement for Long Text Quality: The quality of training data are crucial for enhancing the long-text capabilities of foundation models. Despite existing efforts to refine data quality through heuristic rules and evaluations based on data diversity and difficulty, there's a lack of systematic approaches specifically tailored for assessing long texts. Addressing this gap, our work systematically measures the quality of long texts by evaluating three fundamental linguistic dimensions: coherence, cohesion, and complexity. Drawing inspiration from the aforementioned three dimensions, we introduce a suite of metrics designed to evaluate the quality of long texts, encompassing both statistical and pre-trained language model-based ones. Leveraging these metrics, we present LongWanjuan, a bilingual dataset specifically tailored to enhance the training of language models for long-text tasks with over 160B tokens. In LongWanjuan, we categorize long texts into holistic, aggregated, and chaotic types, enabling a detailed analysis of long-text quality. Furthermore, we devise a data mixture recipe that strategically balances different types of long texts within LongWanjuan, leading to significant improvements in model performance on long-text tasks. The code and dataset are available at https://github.com/OpenLMLab/LongWanjuan.<|reference_end|>
@article{lv2024longwanjuan:,
    title={LongWanjuan: Towards Systematic Measurement for Long Text Quality},
    author={Kai Lv, Xiaoran Liu, Qipeng Guo, Hang Yan, Conghui He, Xipeng Qiu and
  Dahua Lin},
    journal={arXiv preprint arXiv:2402.13583},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13583},
    primaryClass={cs.CL}
}