title: Uni-SMART: Universal Science Multimodal Analysis and Research
    Transformer
summary: 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.
paper_authors._id: 65f7b0e9f78419ddb084224b
paper_authors.name: Hengxing Cai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:55:35.867Z
paper_authors._id: 6340c214181f5648a58f5f82
paper_authors.avatarUrl: /avatars/2ee353d8ec2b0ebc5b58d0641beddc26.svg
paper_authors.fullname: Hengxing Cai
paper_authors.type: user
paper_authors.user: enjoy
paper_authors._id: 65f7b0e9f78419ddb084224c
paper_authors.name: Xiaochen Cai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-18T09:23:50.845Z
paper_authors._id: 64ac17a15769f5f88bbf5088
paper_authors.avatarUrl: /avatars/f8f50390cf767cc62ae612e6b600a340.svg
paper_authors.fullname: caixiaochen
paper_authors.type: user
paper_authors.user: caixc97
paper_authors._id: 65f7b0e9f78419ddb084224d
paper_authors.name: Shuwen Yang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:56:22.333Z
paper_authors._id: 64b9e65184ddd52599c60640
paper_authors.avatarUrl: /avatars/beb000f76e209fffe782ca8411c309bb.svg
paper_authors.fullname: shuwen yang
paper_authors.type: user
paper_authors.user: PKUterran
paper_authors._id: 65f7b0e9f78419ddb084224e
paper_authors.name: Jiankun Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:56:30.874Z
paper_authors._id: 64ffa30813f15465268a4029
paper_authors.avatarUrl: /avatars/8d3218b580fb50236660c957f17e10bd.svg
paper_authors.fullname: Jiankun Wang
paper_authors.type: user
paper_authors.user: Kuhn95
paper_authors._id: 65f7b0e9f78419ddb084224f
paper_authors.name: Lin Yao
paper_authors._id: 65f7b0e9f78419ddb0842250
paper_authors.name: Zhifeng Gao
paper_authors._id: 65f7b0e9f78419ddb0842251
paper_authors.name: Junhan Chang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-18T08:56:13.416Z
paper_authors._id: 65b82e4b1b7c0aaf18aca694
paper_authors.avatarUrl: /avatars/869856209b39047c55ec7369be49d354.svg
paper_authors.fullname: Junhan Chang
paper_authors.type: user
paper_authors.user: changjh
paper_authors._id: 65f7b0e9f78419ddb0842252
paper_authors.name: Sihang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-18T09:02:20.389Z
paper_authors._id: 635913a68f38318bfabc90ea
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/635913a68f38318bfabc90ea/29M50hPy2nMwMjMElAEgA.jpeg
paper_authors.fullname: Sihang Li
paper_authors.type: user
paper_authors.user: Sihangli
paper_authors._id: 65f7b0e9f78419ddb0842253
paper_authors.name: Mingjun Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:57:42.147Z
paper_authors._id: 653b75e286b88947d5d64eff
paper_authors.avatarUrl: /avatars/259e5bd353fdb05d2bd102b6d5c2d589.svg
paper_authors.fullname: Mingjun Xu
paper_authors.type: user
paper_authors.user: xumingjun
paper_authors._id: 65f7b0e9f78419ddb0842254
paper_authors.name: Changxin Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:57:49.048Z
paper_authors._id: 637dcf389763265080396174
paper_authors.avatarUrl: /avatars/c9d8704e2e0af13329604416bd3f1d3a.svg
paper_authors.fullname: Changxin Wang
paper_authors.type: user
paper_authors.user: foril
paper_authors._id: 65f7b0e9f78419ddb0842255
paper_authors.name: Hongshuai Wang
paper_authors._id: 65f7b0e9f78419ddb0842256
paper_authors.name: Yongge Li
paper_authors._id: 65f7b0e9f78419ddb0842257
paper_authors.name: Mujie Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:58:12.477Z
paper_authors._id: 659cb8e689d3646c170baff5
paper_authors.avatarUrl: /avatars/84b72949c11eb696fc759a5337d4047c.svg
paper_authors.fullname: Mujie Lin
paper_authors.type: user
paper_authors.user: Linmj
paper_authors._id: 65f7b0e9f78419ddb0842258
paper_authors.name: Yaqi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:58:18.863Z
paper_authors._id: 659638887542fd1a5ba30c75
paper_authors.avatarUrl: /avatars/4f589f626c204b84f7487ab46ee19a9b.svg
paper_authors.fullname: Yaqi Li
paper_authors.type: user
paper_authors.user: lyq9811
paper_authors._id: 65f7b0e9f78419ddb0842259
paper_authors.name: Yuqi Yin
paper_authors._id: 65f7b0e9f78419ddb084225a
paper_authors.name: Linfeng Zhang
paper_authors._id: 65f7b0e9f78419ddb084225b
paper_authors.name: Guolin Ke
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-18T08:58:48.332Z
paper_authors._id: 6348de0c62c668c7b48d83c9
paper_authors.avatarUrl: /avatars/7296ea9bb301e19c10926022959b2023.svg
paper_authors.fullname: Guolin Ke
paper_authors.type: user
paper_authors.user: guolinke
paper_title: Uni-SMART: Universal Science Multimodal Analysis and Research
    Transformer
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) multimodal analysis molecular structure tables charts Uni-SMART (Universal Science Multimodal Analysis and Research Transformer) patent infringement detection