NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens
This addresses the challenge for researchers and developers in assessing LLMs' long-context abilities, though it is incremental as it builds on existing benchmarking efforts.
The authors tackled the problem of evaluating large language models' long-context understanding by introducing NovelQA, a benchmark based on English novels exceeding 200,000 tokens, which revealed that models struggle with multi-hop reasoning and detail-oriented questions.
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.