SurveyEval: Towards Comprehensive Evaluation of LLM-Generated Academic Surveys

• URL: http://arxiv.org/abs/2512.02763v1
• Date: Tue, 02 Dec 2025 13:42:09 GMT
• Title: SurveyEval: Towards Comprehensive Evaluation of LLM-Generated Academic Surveys
• Authors: Jiahao Zhao, Shuaixing Zhang, Nan Xu, Lei Wang,
• Abstract summary: We introduce SurveyEval, a benchmark that evaluates automatically generated surveys across three dimensions: overall quality, outline coherence, and reference accuracy.<n>We extend the evaluation across 7 subjects and augment the LLM-as-a-Judge framework with human references to strengthen evaluation-human alignment.
• Score: 25.85280799022144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM-based automatic survey systems are transforming how users acquire information from the web by integrating retrieval, organization, and content synthesis into end-to-end generation pipelines. While recent works focus on developing new generation pipelines, how to evaluate such complex systems remains a significant challenge. To this end, we introduce SurveyEval, a comprehensive benchmark that evaluates automatically generated surveys across three dimensions: overall quality, outline coherence, and reference accuracy. We extend the evaluation across 7 subjects and augment the LLM-as-a-Judge framework with human references to strengthen evaluation-human alignment. Evaluation results show that while general long-text or paper-writing systems tend to produce lower-quality surveys, specialized survey-generation systems are able to deliver substantially higher-quality results. We envision SurveyEval as a scalable testbed to understand and improve automatic survey systems across diverse subjects and evaluation criteria.

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