This paper aims to tackle the challenge posed by the increasing integration
of software tools in research across various disciplines by investigating the
application of Falcon-7b for the detection and classification of software
mentions within scholarly texts. Specifically, the study focuses on solving
Subtask I of the Software Mention Detection in Scholarly Publications (SOMD),
which entails identifying and categorizing software mentions from academic
literature. Through comprehensive experimentation, the paper explores different
training strategies, including a dual-classifier approach, adaptive sampling,
and weighted loss scaling, to enhance detection accuracy while overcoming the
complexities of class imbalance and the nuanced syntax of scholarly writing.
The findings highlight the benefits of selective labelling and adaptive
sampling in improving the model's performance. However, they also indicate that
integrating multiple strategies does not necessarily result in cumulative
improvements. This research offers insights into the effective application of
large language models for specific tasks such as SOMD, underlining the
importance of tailored approaches to address the unique challenges presented by
academic text analysis.

通过研究 Falcon-7b 在学术文本中检测和分类软件提及的应用，本研究的主要目标是解决软件提及检测在学术出版物中的子任务一，即从学术文献中识别和分类软件提及。通过综合实验，本论文探讨了不同的训练策略，包括双分类器方法、自适应抽样和加权损失缩放，以提高检测准确性并克服类别不平衡和学术写作的复杂语法等问题。研究结果强调了选择性标注和自适应抽样在改善模型性能方面的益处。然而，研究还表明，整合多种策略并不一定会累积改进。该研究为特定任务如 SOMD 的大型语言模型的有效应用提供了见解，突出了解决学术文本分析中所面临的独特挑战的个性化方法的重要性。