title: AAAR-1.0: Assessing AI's Potential to Assist Research
summary: Numerous studies have assessed the proficiency of AI systems, particularly
  large language models (LLMs), in facilitating everyday tasks such as email
  writing, question answering, and creative content generation. However,
  researchers face unique challenges and opportunities in leveraging LLMs for
  their own work, such as brainstorming research ideas, designing experiments,
  and writing or reviewing papers. In this study, we introduce AAAR-1.0, a
  benchmark dataset designed to evaluate LLM performance in three fundamental,
  expertise-intensive research tasks: (i) EquationInference, assessing the
  correctness of equations based on the contextual information in paper
  submissions; (ii) ExperimentDesign, designing experiments to validate research
  ideas and solutions; (iii) PaperWeakness, identifying weaknesses in paper
  submissions; and (iv) REVIEWCRITIQUE, identifying each segment in human reviews
  is deficient or not. AAAR-1.0 differs from prior benchmarks in two key ways:
  first, it is explicitly research-oriented, with tasks requiring deep domain
  expertise; second, it is researcher-oriented, mirroring the primary activities
  that researchers engage in on a daily basis. An evaluation of both open-source
  and proprietary LLMs reveals their potential as well as limitations in
  conducting sophisticated research tasks. We will keep iterating AAAR-1.0 to new
  versions.
paper_authors._id: 6725315f8c843bff5d786853
paper_authors.name: Renze Lou
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paper_authors.name: Hanzi Xu
paper_authors._id: 6725315f8c843bff5d786855
paper_authors.name: Sijia Wang
paper_authors._id: 6725315f8c843bff5d786856
paper_authors.name: Jiangshu Du
paper_authors._id: 6725315f8c843bff5d786857
paper_authors.name: Ryo Kamoi
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paper_authors.fullname: Ryo Kamoi
paper_authors.type: user
paper_authors.user: ryokamoi
paper_authors._id: 6725315f8c843bff5d786858
paper_authors.name: Xiaoxin Lu
paper_authors._id: 6725315f8c843bff5d786859
paper_authors.name: Jian Xie
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paper_authors.fullname: Jian Xie
paper_authors.type: user
paper_authors.user: hsaest
paper_authors._id: 6725315f8c843bff5d78685a
paper_authors.name: Yuxuan Sun
paper_authors._id: 6725315f8c843bff5d78685b
paper_authors.name: Yusen Zhang
paper_authors._id: 6725315f8c843bff5d78685c
paper_authors.name: Jihyun Janice Ahn
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:38:49.529Z
paper_authors._id: 6621b4db2863a501db0309f8
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paper_authors.fullname: Jihyun Ahn
paper_authors.type: user
paper_authors.user: JJAhn030
paper_authors._id: 6725315f8c843bff5d78685d
paper_authors.name: Hongchao Fang
paper_authors._id: 6725315f8c843bff5d78685e
paper_authors.name: Zhuoyang Zou
paper_authors._id: 6725315f8c843bff5d78685f
paper_authors.name: Wenchao Ma
paper_authors._id: 6725315f8c843bff5d786860
paper_authors.name: Xi Li
paper_authors._id: 6725315f8c843bff5d786861
paper_authors.name: Kai Zhang
paper_authors._id: 6725315f8c843bff5d786862
paper_authors.name: Congying Xia
paper_authors._id: 6725315f8c843bff5d786863
paper_authors.name: Lifu Huang
paper_authors._id: 6725315f8c843bff5d786864
paper_authors.name: Wenpeng Yin
paper_title: AAAR-1.0: Assessing AI's Potential to Assist Research
paper_summary: Numerous studies have assessed the proficiency of AI systems, particularly
  large language models (LLMs), in facilitating everyday tasks such as email
  writing, question answering, and creative content generation. However,
  researchers face unique challenges and opportunities in leveraging LLMs for
  their own work, such as brainstorming research ideas, designing experiments,
  and writing or reviewing papers. In this study, we introduce AAAR-1.0, a
  benchmark dataset designed to evaluate LLM performance in three fundamental,
  expertise-intensive research tasks: (i) EquationInference, assessing the
  correctness of equations based on the contextual information in paper
  submissions; (ii) ExperimentDesign, designing experiments to validate research
  ideas and solutions; (iii) PaperWeakness, identifying weaknesses in paper
  submissions; and (iv) REVIEWCRITIQUE, identifying each segment in human reviews
  is deficient or not. AAAR-1.0 differs from prior benchmarks in two key ways:
  first, it is explicitly research-oriented, with tasks requiring deep domain
  expertise; second, it is researcher-oriented, mirroring the primary activities
  that researchers engage in on a daily basis. An evaluation of both open-source
  and proprietary LLMs reveals their potential as well as limitations in
  conducting sophisticated research tasks. We will keep iterating AAAR-1.0 to new
  versions.
paper_ai_keywords: large language models (LLMs) AAAR-1.0 EquationInference ExperimentDesign PaperWeakness REVIEWCRITIQUE