SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
Abstract: Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis.
However, existing benchmarks fail to adequately evaluate the proficiency of LLMs in this domain, particularly in scenarios requiring higher-level abilities beyond mere memorization and the handling of multimodal data.
In response to this gap, we introduce SciAssess, a benchmark specifically designed for the comprehensive evaluation of LLMs in scientific literature analysis.
It aims to thoroughly assess the efficacy of LLMs by evaluating their capabilities in Memorization (L1), Comprehension (L2), and Analysis \& Reasoning (L3).
It encompasses a variety of tasks drawn from diverse scientific fields, including fundamental science, alloy materials, biomedicine, drug discovery, and organic materials.
To ensure the reliability of SciAssess, rigorous quality control measures have been implemented, ensuring accuracy, anonymization, and compliance with copyright standards.
SciAssess evaluates 11 LLMs, highlighting their strengths and areas for improvement.
We hope this evaluation supports the ongoing development of LLM applications in scientific literature analysis.
SciAssess and its resources are available at \url{https://anonymous.4open.science/r/SciAssess-2D14}.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: Resources and Evaluation
Contribution Types: Data resources, Data analysis
Languages Studied: English
Submission Number: 2309
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