Summary of Enhancing Source Code Classification Effectiveness Via Prompt Learning Incorporating Knowledge Features, by Yong Ma et al.
Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge Features
by Yong Ma, Senlin Luo, Yu-Ming Shang, Yifei Zhang, Zhengjun Li
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed paper introduces a novel approach to text classification called CodeClassPrompt, which leverages pre-trained language models like CodeBERT to enhance source code-related tasks. By utilizing prompt learning and attention mechanisms, the method extracts rich knowledge associated with input sequences, eliminating the need for additional neural network layers and reducing computational costs. The approach is tested across four distinct source code-related tasks, achieving competitive performance while significantly lowering computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to analyze computer code using pre-trained language models like CodeBERT. Currently, these models don’t use all the information they have about code, which limits how well they can classify different types of code. The researchers created a new method called CodeClassPrompt that makes better use of this information. It uses something called prompt learning and attention mechanisms to extract important details from the code. This approach is faster and more accurate than previous methods. |
Keywords
» Artificial intelligence » Attention » Neural network » Prompt » Text classification
