NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
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Abstract (arXiv)
Recent advancements in Large Language Models (LLMs) have pushed the boundaries of natural language processing, especially in long-context understanding. 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 tailored for evaluating LLMs with complex, extended narratives. 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 details the design and construction of NovelQA, focusing on its comprehensive manual annotation process and the variety of question types aimed at evaluating nuanced comprehension. Our evaluation of long-context LLMs on NovelQA reveals significant insights into their strengths and weaknesses. Notably, the models struggle with multi-hop reasoning, detail-oriented questions, and handling extremely long inputs, with average lengths exceeding 200,000 tokens. Results highlight the need for substantial advancements in LLMs to enhance their long-context comprehension and contribute effectively to computational literary analysis.
Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 39 of its 58 references that are in the map. First posted on arXiv 18 Mar 2024.
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39 of 58 references.
References · 39
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Citing papers per year: 2024: 1; 2025: 18; 2026: 13.
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- 2026LMEB: Long-horizon Memory Embedding Benchmark13 Mar 2026
- 2026DySCO: Dynamic Attention-Scaling Decoding for Long-Context Language Models25 Feb 2026
- 2026The Pensieve Paradigm: Stateful Language Models Mastering Their Own Context12 Feb 2026
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- 2025LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA15 Oct 2025
- 2025NarraBench: A Comprehensive Framework for Narrative Benchmarking10 Oct 2025
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