HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
<|reference_start|>HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models: In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks (e.g., long-context understanding), and many benchmarks have been proposed. However, we observe that long text generation capabilities are not well investigated. Therefore, we introduce the Hierarchical Long Text Generation Benchmark (HelloBench), a comprehensive, in-the-wild, and open-ended benchmark to evaluate LLMs' performance in generating long text. Based on Bloom's Taxonomy, HelloBench categorizes long text generation tasks into five subtasks: open-ended QA, summarization, chat, text completion, and heuristic text generation. Besides, we propose Hierarchical Long Text Evaluation (HelloEval), a human-aligned evaluation method that significantly reduces the time and effort required for human evaluation while maintaining a high correlation with human evaluation. We have conducted extensive experiments across around 30 mainstream LLMs and observed that the current LLMs lack long text generation capabilities. Specifically, first, regardless of whether the instructions include explicit or implicit length constraints, we observe that most LLMs cannot generate text that is longer than 4000 words. Second, we observe that while some LLMs can generate longer text, many issues exist (e.g., severe repetition and quality degradation). Third, to demonstrate the effectiveness of HelloEval, we compare HelloEval with traditional metrics (e.g., ROUGE, BLEU, etc.) and LLM-as-a-Judge methods, which show that HelloEval has the highest correlation with human evaluation. We release our code in https://github.com/Quehry/HelloBench.<|reference_end|>
@article{que2024hellobench:,
    title={HelloBench: Evaluating Long Text Generation Capabilities of Large
  Language Models},
    author={Haoran Que, Feiyu Duan, Liqun He, Yutao Mou, Wangchunshu Zhou, Jiaheng
  Liu, Wenge Rong, Zekun Moore Wang, Jian Yang, Ge Zhang, Junran Peng,
  Zhaoxiang Zhang, Songyang Zhang, Kai Chen},
    journal={arXiv preprint arXiv:2409.16191},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16191},
    primaryClass={cs.CL}
}