AutoPaperBench: An MLLM-Based Framework for Automatic Generation of Paper Understanding Evaluation Benchmarks
Min-Woo Kim, H.S. Park, Hee-Jin Ahn, Woo-Ram Park, Jae-Wan Jeon, Kyong-Ha Lee, Ryong Lee, Dong‐Geol Choi · Electronics · 2025
AutoPaperBench proposes a benchmark generation system to automatically evaluate the comprehensibility of papers in a Multimodal Large Language Model (MLLM). The proposed system efficiently structures the content of a paper through semantic parsing and automatically generates text-based QAs and visual-based VQAs. To ensure the quality of the generated QA, we introduce a reviewer system that evaluates six criteria such as logic and appropriateness. In our experiments on 60 research papers from the medical, natural, and engineering fields, the generated benchmarks demonstrate comparable performance rankings to those of previous benchmarks, and the performance improvements achieved through semantic parsing are validated. The system can run on a single GPU environment and provides a framework for efficiently evaluating LLM thesis comprehension.