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New benchmark SurGE evaluates LLMs for scientific survey generation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SurGE, a new benchmark and evaluation framework designed to assess the capabilities of large language models in generating scientific surveys. The framework includes a dataset of test instances with topic descriptions and expert-written surveys, alongside a corpus of over one million academic papers. An automated evaluation system measures generated surveys on comprehensiveness, citation accuracy, organization, and content quality, revealing that current advanced models still face significant challenges in this domain. AI

IMPACT Establishes a new standard for evaluating LLM performance in academic survey generation, potentially guiding future research and development.

RANK_REASON This is a research paper introducing a new benchmark and evaluation framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark SurGE evaluates LLMs for scientific survey generation

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This is a research paper introducing a new benchmark and evaluation framework for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Weihang Su, Anzhe Xie, Qingyao Ai, Jianming Long, Xuanyi Chen, Jiaxin Mao, Ziyi Ye, Yiqun Liu ·

    SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation

    arXiv:2508.15658v5 Announce Type: replace Abstract: The rapid growth of academic literature makes the manual creation of scientific surveys increasingly infeasible. While large language models show promise for automating this process, progress in this area is hindered by the abse…

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