- 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. - This is especially true in areas like drug discovery, where researchers have to search through a large amount of literature to find key information about targets and active molecules. - 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, including chemistry, biology, and materials science, Uni-SMART demonstrates superior performance over leading text-only LLMs. - Furthermore, our exploration extends to practical applications, including patent infringement detection and nuanced analysis of charts in material research. - These applications not only highlight Uni-SMART's versatility but also its potential to revolutionize how we interact with scientific literature. - By setting new benchmarks and offering insights into its practical utility, Uni-SMART stands at the forefront of advancing multimodal understanding in scientific research, paving the way for more informed discoveries and innovations. -
+ 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. +