This study introduces a novel software tool leveraging large language model
(LLM) prompts, designed to automate the generation of academic articles from
Python code a significant advancement in the fields of biomedical informatics
and computer science. Selected for its widespread adoption and analytical
versatility, Python served as a foundational proof of concept; however, the
underlying methodology and framework exhibit adaptability across various GitHub
repo's underlining the tool's broad applicability (Harper 2024). By mitigating
the traditionally time-intensive academic writing process, particularly in
synthesizing complex datasets and coding outputs, this approach signifies a
monumental leap towards streamlining research dissemination. The development
was achieved without reliance on advanced language model agents, ensuring high
fidelity in the automated generation of coherent and comprehensive academic
content. This exploration not only validates the successful application and
efficiency of the software but also projects how future integration of LLM
agents which could amplify its capabilities, propelling towards a future where
scientific findings are disseminated more swiftly and accessibly.

介绍了一种利用大型语言模型的新软件工具，旨在自动从 Python 代码生成学术文章，这对生物医学信息学和计算机科学领域具有重要意义，可大大提高研究传播效率。

科学出版的未来：自动文章生成

The Future of Scientific Publishing: Automated Article Generation

With the widespread use of the internet, it has become increasingly crucial
to extract specific information from vast amounts of academic articles
efficiently. Data mining techniques are generally employed to solve this issue.
However, data mining for academic articles is challenging since it requires
automatically extracting specific patterns in complex and unstructured layout
documents. Current data mining methods for academic articles employ
rule-based(RB) or machine learning(ML) approaches. However, using rule-based
methods incurs a high coding cost for complex typesetting articles. On the
other hand, simply using machine learning methods requires annotation work for
complex content types within the paper, which can be costly. Furthermore, only
using machine learning can lead to cases where patterns easily recognized by
rule-based methods are mistakenly extracted. To overcome these issues, from the
perspective of analyzing the standard layout and typesetting used in the
specified publication, we emphasize implementing specific methods for specific
characteristics in academic articles. We have developed a novel Text Block
Refinement Framework (TBRF), a machine learning and rule-based scheme hybrid.
We used the well-known ACL proceeding articles as experimental data for the
validation experiment. The experiment shows that our approach achieved over 95%
classification accuracy and 90% detection accuracy for tables and figures.

本论文提出了一种机器学习与基于规则的混合方法，称为 TBRF，用于数据挖掘学术论文中的特定信息，通过分析学术论文中的标准布局和排版方法，通过实验验证表明，该方法可以在分类准确率和表格及图片检测准确率方面达到超过 95% 和 90% 的精度。

从学术文章中提炼文本分类和物体识别的框架

A Framework For Refining Text Classification and Object Recognition from  Academic Articles

Blockchains provide a mechanism through which mutually distrustful remote
parties can reach consensus on the state of a ledger of information. With the
great acceleration with which this space is developed, the demand for those
seeking to learn about blockchain also grows. Being a technical subject, it can
be quite intimidating to start learning. For this reason, the main objective of
this project was to apply machine learning models to extract information from
whitepapers and academic articles focused on the blockchain area to organize
this information and aid users to navigate the space.

该项目的主要目标是应用机器学习模型从区块链领域的白皮书和学术文章中提取信息，以组织这些信息并帮助用户浏览该领域。

从文本中提取区块链概念

Extracting Blockchain Concepts from Text

Purpose
The purpose of this paper is to explore which structures of academic articles
referees would pay more attention to, what specific content referees focus on,
and whether the distribution of PRC is related to the citations.
Design/methodology/approach
Firstly, utilizing the feature words of section title and hierarchical
attention network model (HAN) to identify the academic article structures.
Secondly, analyzing the distribution of PRC in different structures according
to the position information extracted by rules in PRC. Thirdly, analyzing the
distribution of feature words of PRC extracted by the Chi-square test and
TF-IDF in different structures. Finally, four correlation analysis methods are
used to analyze whether the distribution of PRC in different structures is
correlated to the citations. Findings
The count of PRC distributed in Materials and Methods and Results section is
significantly more than that in the structure of Introduction and Discussion,
indicating that referees pay more attention to the Material and Methods and
Results. The distribution of feature words of PRC in different structures is
obviously different, which can reflect the content of referees' concern. There
is no correlation between the distribution of PRC in different structures and
the citations. Research limitations/implications
Due to the differences in the way referees write peer review reports, the
rules used to extract position information cannot cover all PRC.
Originality/value
The paper finds a pattern in the distribution of PRC in different academic
article structures proving the long-term empirical understanding. It also
provides insight into academic article writing: researchers should ensure the
scientificity of methods and the reliability of results when writing academic
article to obtain a high degree of recognition from referees.

本文利用 HAN 模型和 Chi-square 测试进行语料分析，发现论文提供的 Method 和 Results 结构更受审稿人的关注，同时不同结构的 PRC 分布与论文被引用次数之间不存在相关性，研究提示：作者在论文写作中应该保证方法的科学性和结果的可靠性以赢得审稿人的认可。