Summary of Investigating the Transferability Of Code Repair For Low-resource Programming Languages, by Kyle Wong et al.
Investigating the Transferability of Code Repair for Low-Resource Programming Languages
by Kyle Wong, Alfonso Amayuelas, Liangming Pan, William Yang Wang
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Large language models (LLMs) have demonstrated impressive performance on code generation tasks, particularly iterative code repair. Recent works integrate techniques like chain-of-thought reasoning or distillation to enhance the code repair process. However, these studies focus solely on high-resource languages like Python, neglecting low-resource languages like Perl. To address this knowledge gap, our study investigates the benefits of distilling code repair for both high- and low-resource languages. We find that distilling code repair has language-dependent benefits, contradicting previous assumptions. A deeper analysis reveals a weak correlation between reasoning ability and code correction ability, which is exacerbated in low-resource settings where base models lack deep knowledge of a programming language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart at writing code! They can even fix mistakes by thinking about what went wrong and writing new code. Some people tried making these models better at fixing code by using special tricks like “chain-of-thought reasoning” or “distillation”. But they only tested it on languages like Python, not weird ones like Perl. We wanted to see if the same tricks would work for all kinds of programming languages. Our results show that making the model better at fixing code works differently depending on the language! We looked deeper and found out that there’s actually no strong connection between being good at thinking and being good at fixing code. This is especially true when we’re dealing with weird languages like Perl. |
Keywords
» Artificial intelligence » Distillation
