Summary of Read Between the Lines — Functionality Extraction From Readmes, by Prince Kumar et al.
Read between the lines – Functionality Extraction From READMEs
by Prince Kumar, Srikanth Tamilselvam, Dinesh Garg
First submitted to arxiv on: 15 Mar 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 A novel text-to-text generation task called functionality extraction from Git README files is introduced, which involves peculiarities and challenges that existing systems are not well-suited to handle. This task arises from recent research into large language models for code-related tasks, such as code refactoring and summarization. A human-annotated dataset called FuncRead is released, along with a range of models developed for this task. Fine-tuned models outperform baselines using popular LLMs like ChatGPT and Bard, with the best fine-tuned CodeLlama model achieving 70% and 20% gains on F1 score compared to ChatGPT and Bard respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to summarize text called functionality extraction from Git README files. It’s different because it involves unique challenges that existing systems aren’t good at handling. This task is important because researchers are using big language models for tasks like code refactoring and summarization. The authors release a dataset they’ve labeled themselves, along with some models they developed for this task. They found that smaller models that were trained specifically for this task did better than bigger models like ChatGPT and Bard. |
Keywords
» Artificial intelligence » F1 score » Summarization » Text generation
