XL$^2$Bench: A Benchmark for Extremely Long Context Understanding with Long-range Dependencies
Abstract: Recently, various efforts have been proposed to expand the context window size of large language models (LLMs). Meanwhile, building high-quality benchmarks with much longer text lengths and more demanding tasks to provide comprehensive evaluations is of immense practical interest to facilitate long context understanding research of LLMs. However, prior benchmarks create datasets that ostensibly cater to long-text comprehension by expanding the input of traditional tasks, which falls short to exhibit the unique characteristics of long-text understanding, including long dependency tasks and longer text length compatible with modern LLMs' context window size. In this paper, we introduce a benchmark for extremely long context understanding with long-range dependencies, XL$^2$Bench, which includes three scenarios—Fiction Reading, Paper Reading, and Law Reading—and four tasks of increasing complexity: Memory Retrieval, Detailed Understanding, Overall Understanding, and Open-ended Generation, covering 27 subtasks in English and Chinese. It has an average length of 100K+ words (English) and 200K+ characters (Chinese). Evaluating seven leading LLMs on XL$^2$Bench, we find that their performance significantly lags behind human levels. Moreover, the observed decline in performance across both the original and enhanced datasets underscores the efficacy of our approach to mitigating data contamination.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: benchmarking, automatic creation and evaluation of language resources, evaluation
Contribution Types: Data resources
Languages Studied: English, Chinese
Submission Number: 4646
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