LooGLE v2: Are LLMs Ready for Real World Long Dependency Challenges?
ZiyuanHe, Yuxuan Wang, Jiaqi Li, Kexin Liang, Muhan Zhang
Large language models (LLMs) are equipped with increasingly extended context windows recently, yet their long context understanding capabilities over long dependency tasks remains fundamentally limited and underexplored. This gap is especially significant in many real-world long-context applications that were rarely benchmarked. In this paper, we introduce \textbf{LooGLE v2}, a novel benchmark designed to evaluate LLMs' long context ability in real-world applications and scenarios. Our benchmark consists of automatically collected real-world long texts, ranging from 16k to 2M tokens, encompassing domains in law, finance, game and code. Accordingly, we delicately design 10 types of domain-specific long-dependency tasks and generate 1,934 QA instances with various diversity and complexity in a scalable data curation pipeline for further practical needs. We make comprehensive assessment on 6 locally deployed and 4 API-based LLMs. The evaluation results show that even the the best-performing model achieves only a 59.2\% overall score on our benchmark. Despite the extensive context windows, popular LLMs are only capable of understanding a much shorter length of context than they claim to be, revealing significant limitations in their ability to handle real-world tasks with long-range dependencies and highlighting substantial room for model improvement in practical long-context understanding.