In scientific research and its application, scientific literature analysis is
crucial as it allows researchers to build on the work of others. However, the
fast growth of scientific knowledge has led to a massive increase in scholarly
articles, making in-depth literature analysis increasingly challenging and
time-consuming. The emergence of Large Language Models (LLMs) has offered a new
way to address this challenge. Known for their strong abilities in summarizing
texts, LLMs are seen as a potential tool to improve the analysis of scientific
literature. However, existing LLMs have their own limits. Scientific literature
often includes a wide range of multimodal elements, such as molecular
structure, tables, and charts, which are hard for text-focused LLMs to
understand and analyze. This issue points to the urgent need for new solutions
that can fully understand and analyze multimodal content in scientific
literature. To answer this demand, we present Uni-SMART (Universal Science
Multimodal Analysis and Research Transformer), an innovative model designed for
in-depth understanding of multimodal scientific literature. Through rigorous
quantitative evaluation across several domains, Uni-SMART demonstrates superior
performance over leading text-focused LLMs. Furthermore, our exploration
extends to practical applications, including patent infringement detection and
nuanced analysis of charts. These applications not only highlight Uni-SMART's
adaptability but also its potential to revolutionize how we interact with
scientific literature.

Uni-SMART 是一种创新模型，旨在深入理解多模式科学文献，通过量化评估在多个领域展示了超越文本焦点的大型语言模型的卓越性能，同时探索了专利侵权检测和图表的细致分析等实际应用，凸显了 Uni-SMART 的适应性和革新性。

Uni-SMART: 通用科学多模态分析与研究变换器

Uni-SMART: Universal Science Multimodal Analysis and Research  Transformer

Recent breakthroughs in Large Language Models (LLMs) have revolutionized
natural language understanding and generation, igniting a surge of interest in
leveraging these technologies for the nuanced field of scientific literature
analysis. Existing benchmarks, however, inadequately evaluate the proficiency
of LLMs in the scientific domain, especially in scenarios involving complex
comprehension and multimodal data. In response, we introduced SciAssess, a
benchmark tailored for the in-depth analysis of scientific literature, crafted
to provide a thorough assessment of LLMs' efficacy. SciAssess focuses on
evaluating LLMs' abilities in memorization, comprehension, and analysis within
scientific contexts. It includes representative tasks from diverse scientific
fields, such as general chemistry, organic materials, and alloy materials. And
rigorous quality control measures ensure its reliability in terms of
correctness, anonymization, and copyright compliance. SciAssess evaluates
leading LLMs, including GPT-4, GPT-3.5-turbo, and Gemini, identifying their
strengths and areas for improvement and supporting the ongoing development of
LLM applications in scientific literature analysis. SciAssess and its resources
are made available at this https URL, offering a valuable tool
for advancing LLM capabilities in scientific literature analysis.

最近大规模语言模型（LLMs）的突破性进展在自然语言理解和生成方面带来了革命性的变化，引发了对将这些技术应用于科学文献分析这一细分领域的兴趣激增。然而，现有的基准不足以充分评估 LLM 在科学领域，尤其是在涉及复杂理解和多模态数据的情景中的能力。本文介绍了 SciAssess，这是一个专门用于深入分析科学文献的基准，旨在对 LLM 的效能进行全面评估。SciAssess 专注于评估 LLM 在科学背景下的记忆、理解和分析能力。它包括来自不同科学领域的代表性任务，例如普通化学、有机材料和合金材料。严格的质量控制措施确保了其在正确性、匿名性和版权合规性方面的可靠性。SciAssess 评估了领先的 LLM，包括 GPT-4、GPT-3.5-turbo 和 Gemini，并确定了它们的优势和改进的领域，支持 LLM 在科学文献分析领域的持续发展。SciAssess 及其资源可在此 https URL 上获得，为推进 LLM 在科学文献分析中的能力提供了一个有价值的工具。