EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants

• URL: http://arxiv.org/abs/2502.20309v1
• Date: Thu, 27 Feb 2025 17:35:57 GMT
• Title: EAIRA: Establishing a Methodology for Evaluating AI Models as Scientific Research Assistants
• Authors: Franck Cappello, Sandeep Madireddy, Robert Underwood, Neil Getty, Nicholas Lee-Ping Chia, Nesar Ramachandra, Josh Nguyen, Murat Keceli, Tanwi Mallick, Zilinghan Li, Marieme Ngom, Chenhui Zhang, Angel Yanguas-Gil, Evan Antoniuk, Bhavya Kailkhura, Minyang Tian, Yufeng Du, Yuan-Sen Ting, Azton Wells, Bogdan Nicolae, Avinash Maurya, M. Mustafa Rafique, Eliu Huerta, Bo Li, Ian Foster, Rick Stevens,
• Abstract summary: This paper describes a multifaceted methodology for evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory.<n>It incorporates four primary classes of evaluations. 1) Multiple Choice Questions to assess factual recall; 2) Open Response to evaluate advanced reasoning and problem-solving skills; 3) Lab-Style Experiments involving detailed analysis of capabilities as research assistants in controlled environments; and 4) Field-Style Experiments to capture researcher-LLM interactions at scale in a wide range of scientific domains and applications.
• Score: 13.939979359408557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements have positioned AI, and particularly Large Language Models (LLMs), as transformative tools for scientific research, capable of addressing complex tasks that require reasoning, problem-solving, and decision-making. Their exceptional capabilities suggest their potential as scientific research assistants but also highlight the need for holistic, rigorous, and domain-specific evaluation to assess effectiveness in real-world scientific applications. This paper describes a multifaceted methodology for Evaluating AI models as scientific Research Assistants (EAIRA) developed at Argonne National Laboratory. This methodology incorporates four primary classes of evaluations. 1) Multiple Choice Questions to assess factual recall; 2) Open Response to evaluate advanced reasoning and problem-solving skills; 3) Lab-Style Experiments involving detailed analysis of capabilities as research assistants in controlled environments; and 4) Field-Style Experiments to capture researcher-LLM interactions at scale in a wide range of scientific domains and applications. These complementary methods enable a comprehensive analysis of LLM strengths and weaknesses with respect to their scientific knowledge, reasoning abilities, and adaptability. Recognizing the rapid pace of LLM advancements, we designed the methodology to evolve and adapt so as to ensure its continued relevance and applicability. This paper describes the methodology state at the end of February 2025. Although developed within a subset of scientific domains, the methodology is designed to be generalizable to a wide range of scientific domains.

Related papers

• The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist [3.7803247326675162]
  Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs)<n>This survey proposes a comprehensive framework to categorize the evolving roles of LLMs in scientific innovation across three hierarchical levels: Evaluator, Collaborator, and Scientist.
  arXiv  Detail & Related papers  (2025-07-16T00:11:01Z)
• AI4Research: A Survey of Artificial Intelligence for Scientific Research [55.5452803680643]
  We present a comprehensive survey on AI for Research (AI4Research)<n>We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research.<n>We identify key research gaps and highlight promising future directions.
  arXiv  Detail & Related papers  (2025-07-02T17:19:20Z)
• Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team [53.38438460574943]
  IDVSCI is a multi-agent framework built on large language models (LLMs)<n>It incorporates two key innovations: a Dynamic Knowledge Exchange mechanism and a Dual-Diversity Review paradigm.<n>Results show that IDVSCI consistently achieves the best performance across two datasets.
  arXiv  Detail & Related papers  (2025-06-23T07:12:08Z)
• ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows [82.07367406991678]
  Large Language Models (LLMs) have extended their impact beyond Natural Language Processing.<n>Among these, computer-using agents are capable of interacting with operating systems as humans do.<n>We introduce ScienceBoard, which encompasses a realistic, multi-domain environment featuring dynamic and visually rich scientific software.
  arXiv  Detail & Related papers  (2025-05-26T12:27:27Z)
• Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery [35.888956949646]
  Large Language Models (LLMs) are transforming scientific research by reshaping the scientific method.<n>LLMs are involved in experimental design, data analysis, and enhancing productivity, particularly in chemistry and biology.<n>Transition to AI-driven science raises ethical questions about creativity, oversight, and responsibility.
  arXiv  Detail & Related papers  (2025-05-22T10:05:48Z)
• Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents [11.74019905854637]
  Large language models (LLMs) are evolving into scientific agents that automate critical tasks. Unlike general-purpose LLMs, specialized agents integrate domain-specific knowledge, advanced tool sets, and robust validation mechanisms. We highlight why they differ from general agents and the ways in which they advance research across various scientific fields.
  arXiv  Detail & Related papers  (2025-03-31T13:11:28Z)
• Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
  A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
  arXiv  Detail & Related papers  (2025-02-07T18:26:45Z)
• Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning [51.11965014462375]
  Multimodal Large Language Models (MLLMs) integrate text, images, and other modalities.<n>This paper argues that MLLMs can significantly advance scientific reasoning across disciplines such as mathematics, physics, chemistry, and biology.
  arXiv  Detail & Related papers  (2025-02-05T04:05:27Z)
• Towards Efficient Large Language Models for Scientific Text: A Review [4.376712802685017]
  Large language models (LLMs) have ushered in a new era for processing complex information in various fields, including science. Due to the power of LLMs, they require extremely expensive computational resources, intense amounts of data, and training time. In recent years, researchers have proposed various methodologies to make scientific LLMs more affordable.
  arXiv  Detail & Related papers  (2024-08-20T10:57:34Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents [49.74065769505137]
  We introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery. It includes 120 different challenge tasks spanning eight topics each with three levels of difficulty and several parametric variations. We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks.
  arXiv  Detail & Related papers  (2024-06-10T20:08:44Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
  Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
  arXiv  Detail & Related papers  (2024-01-26T05:33:34Z)
• The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 [0.0]
  This report focuses on GPT-4, the state-of-the-art language model. We evaluate GPT-4's knowledge base, scientific understanding, scientific numerical calculation abilities, and various scientific prediction capabilities.
  arXiv  Detail & Related papers  (2023-11-13T14:26:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.