Falcon 7b for Software Mention Detection in Scholarly Documents
AmeerAli Khan, Qusai Ramadan, Cong Yang, Zeyd Boukhers

TL;DR
This study evaluates Falcon-7b's effectiveness in detecting and classifying software mentions in scholarly articles, emphasizing tailored training strategies to improve accuracy amidst complex academic language.
Contribution
It introduces a dual-classifier and adaptive sampling approach for software mention detection, providing insights into optimizing large language models for scholarly text analysis.
Findings
Selective labelling improves detection accuracy
Adaptive sampling enhances model performance
Combining multiple strategies yields limited additional benefits
Abstract
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,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Software Engineering Research · Natural Language Processing Techniques
