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Evaluation Sheet for Deep Research: A Use Case for Academic Survey Writing

arXiv 2510.01283 ↗First posted 30 Sep 2025cs.CLcited by 0

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Abstract (arXiv)

Large Language Models (LLMs) powered with argentic capabilities are able to do knowledge-intensive tasks without human involvement. A prime example of this tool is Deep research with the capability to browse the web, extract information and generate multi-page reports. In this work, we introduce an evaluation sheet that can be used for assessing the capability of Deep Research tools. In addition, we selected academic survey writing as a use case task and evaluated output reports based on the evaluation sheet we introduced. Our findings show the need to have carefully crafted evaluation standards. The evaluation done on OpenAI`s Deep Search and Google's Deep Search in generating an academic survey showed the huge gap between search engines and standalone Deep Research tools, the shortcoming in representing the targeted area.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 11 references that are in the map. First posted on arXiv 30 Sep 2025.

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References · 4

  1. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  2. 2025Agentic Systems: A Guide to Transforming Industries with Vertical AI Agentscited by 20
  3. 2023Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunitiescited by 11
  4. 2024State of NLP in Kenya: A Surveycited by 7

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