NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
<|reference_start|>NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens: The rapid advancement of Large Language Models (LLMs) has introduced a new frontier in natural language processing, particularly in understanding and processing long-context information. However, the evaluation of these models' long-context abilities remains a challenge due to the limitations of current benchmarks. To address this gap, we introduce NovelQA, a benchmark specifically designed to test the capabilities of LLMs with extended texts. Constructed from English novels, NovelQA offers a unique blend of complexity, length, and narrative coherence, making it an ideal tool for assessing deep textual understanding in LLMs. This paper presents the design and construction of NovelQA, highlighting its manual annotation, and diverse question types. Our evaluation of Long-context LLMs on NovelQA reveals significant insights into the models' performance, particularly emphasizing the challenges they face with multi-hop reasoning, detail-oriented questions, and extremely long input with an average length more than 200,000 tokens. The results underscore the necessity for further advancements in LLMs to improve their long-context comprehension.<|reference_end|>
@article{wang2024novelqa:,
    title={NovelQA: Benchmarking Question Answering on Documents Exceeding 200K
  Tokens},
    author={Cunxiang Wang, Ruoxi Ning, Boqi Pan, Tonghui Wu, Qipeng Guo, Cheng
  Deng, Guangsheng Bao, Xiangkun Hu, Zheng Zhang, Qian Wang, Yue Zhang},
    journal={arXiv preprint arXiv:2403.12766},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12766},
    primaryClass={cs.CL}
}