SurveyLens: New Benchmark Tests Automatic Survey Generation Across Academic Disciplines
Researchers have introduced SurveyLens, the first discipline-aware benchmark for evaluating automatic survey generation (ASG) systems, comprising 1,000 human-written surveys across 10 academic fields. The benchmark addresses a gap in existing evaluations, which have largely focused on computer science or used generic criteria. The work provides practical guidance for researchers and developers choosing AI tools for discipline-specific literature review tasks.
SurveyLens is a new benchmark designed to assess how well AI systems can automatically generate comprehensive academic literature surveys across diverse disciplines. The benchmark includes SurveyLens-1k, a curated dataset of 1,000 human-written surveys spanning 10 disciplines, and employs a dual-lens evaluation framework that combines discipline-aware rubric scoring with reference-based alignment to human-written surveys. The researchers evaluated 11 state-of-the-art systems representing three paradigms: vanilla large language models (LLMs), specialized ASG systems, and Deep Research agents. Key findings show that Deep Research agents are the only paradigm that performs robustly across all 10 disciplines, while ASG systems lead in structural planning. Critically, all three paradigms showed consistent weakness in reference quality, a finding with direct implications for academic use. The study offers concrete guidance for selecting discipline-appropriate tools and highlights priorities for future ASG system design.
What's missing
The paper does not detail how the human-written surveys were curated or whether they are balanced across disciplines. The evaluation rubrics used for discipline-aware scoring are not described in the abstract, leaving open questions about how discipline-specific standards were operationalized. Additionally, the benchmark's coverage of non-English academic literature is unaddressed.
What different sources said
- arXiv cs.CLCenter
SurveyLens: A Discipline-Aware Benchmark for Automatic Survey Generation
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