Summary of Ircoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators, by Indraneil Paul et al.
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators
by Indraneil Paul, Goran Glavaš, Iryna Gurevych
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Programming Languages (cs.PL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 approach to improving the multilingual capabilities of language models for code generation is proposed. The study investigates the potential of leveraging compiler intermediate representations (IR) shared across programming languages to enhance the transferability of code-language models (Code-LMs). Specifically, researchers explore how exploiting IR data can facilitate cross-lingual transfer and improve the performance of Code-LMs on tasks such as code completion and generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make language models for coding more effective at working with different programming languages. The goal is to develop a way to use compiler intermediate representations (IR) – which are similar across many programming languages – to help language models understand and generate code in various languages. This could improve the ability of these models to learn from code in one language and apply that learning to other languages. |
Keywords
» Artificial intelligence » Transferability
