Summary of Falcon 7b For Software Mention Detection in Scholarly Documents, by Ameerali Khan and Qusai Ramadan and Cong Yang and Zeyd Boukhers
Falcon 7b for Software Mention Detection in Scholarly Documents
by AmeerAli Khan, Qusai Ramadan, Cong Yang, Zeyd Boukhers
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Digital Libraries (cs.DL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study explores the use of Falcon-7b, a large language model, for detecting and categorizing software mentions in scholarly texts. The researchers tackle Subtask I of the Software Mention Detection in Scholarly Publications (SOMD), focusing on identifying and classifying software mentions from academic literature. They investigate various training strategies, including dual-classifier approaches, adaptive sampling, and weighted loss scaling, to improve detection accuracy despite complexities like class imbalance and nuanced syntax. The findings highlight the benefits of selective labelling and adaptive sampling, but also indicate that combining multiple strategies doesn’t always lead to cumulative improvements. This research offers insights into the effective application of large language models for tasks like SOMD, emphasizing the importance of tailored approaches for academic text analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to use a special kind of computer program (called Falcon-7b) to find and group mentions of software in scientific papers. The researchers want to solve one part of this problem (called Subtask I), which involves identifying and categorizing software mentions from academic texts. They try different ways of training the program, such as using two separate programs working together or adjusting how much weight is given to each piece of information. The results show that being selective about what you label as “software mention” and using adaptive sampling can help improve the program’s accuracy. However, they also find that combining multiple strategies doesn’t always make things better. This research helps us understand how to use these special computer programs for specific tasks like this one. |
Keywords
» Artificial intelligence » Large language model » Syntax
