SurveyLens: New Benchmark Tests Automatic Survey Generation Across Academic Disciplines
Researchers introduced SurveyLens, a benchmark for evaluating systems that automatically generate academic literature surveys across 10 different disciplines. The benchmark includes 1,000 human-written surveys and tests 11 state-of-the-art systems using discipline-aware evaluation criteria. The findings reveal that Deep Research agents perform most consistently across disciplines, but all systems struggle with reference quality.
SurveyLens addresses a gap in evaluating Automatic Survey Generation (ASG) systems, which aim to produce comprehensive literature reviews by retrieving, organizing, and synthesizing academic papers. Previous evaluations focused primarily on Computer Science or used generic criteria that may not reflect discipline-specific standards. The benchmark comprises SurveyLens-1k, a curated dataset of 1,000 human-written surveys spanning 10 academic disciplines, paired with a dual-lens evaluation framework combining discipline-aware rubric scoring and reference-based alignment to human surveys. Testing 11 systems across three paradigms—vanilla large language models, specialized ASG systems, and Deep Research agents—the researchers found that Deep Research agents demonstrated the most robust performance across all disciplines, while ASG systems excelled at structural planning. However, all paradigms showed significant weakness in reference quality, offering practical guidance for selecting discipline-specific tools and informing future ASG system design.
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- arXiv cs.CLCenter
SurveyLens: A Discipline-Aware Benchmark for Automatic Survey Generation
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