Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts

• URL: http://arxiv.org/abs/2508.19578v1
• Date: Wed, 27 Aug 2025 05:23:22 GMT
• Title: Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts
• Authors: Jiaqi Deng, Yuho Lee, Nicole Hee-Yeon Kim, Hyangsuk Min, Taewon Yun, Minjeong Ban, Kim Yul, Hwanjun Song,
• Abstract summary: HAMLET is a framework for evaluating the long-context comprehension of large language models.<n>It structures texts into a three-level key-fact hierarchy at root, branch, and leaf-levels.<n>It employs query-focused summarization to evaluate how well models recall and faithfully represent information at each level.
• Score: 19.640586886024952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce HAMLET, a holistic and automated framework for evaluating the long-context comprehension of large language models (LLMs). HAMLET structures source texts into a three-level key-fact hierarchy at root-, branch-, and leaf-levels, and employs query-focused summarization to evaluate how well models recall and faithfully represent information at each level. To validate the reliability of our fully automated pipeline, we conduct a systematic human study, showing that our automatic evaluation achieves over 90% agreement with expert human judgments, while reducing the cost by up to 25 times. HAMLET reveals that LLMs struggle with fine-grained comprehension, especially at the leaf level, and are sensitive to positional effects like the lost-in-the-middle. Analytical queries pose greater challenges than narrative ones, and consistent performance gaps emerge between open-source and proprietary models, as well as across model scales. Our code and dataset are publicly available at https://github.com/DISL-Lab/HAMLET.

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