Multilingual and Multitask Advancements in Code LLMs

Enhancing Multilingual and Multitask Capabilities in Code Large Language Models

Recent advancements in the field of code Large Language Models (LLMs) are significantly enhancing their ability to handle multilingual and multitask scenarios. The focus has shifted towards improving the models' capabilities in code translation, debugging, and web-based tasks, leveraging innovative techniques such as intermediate translations, massively multilingual benchmarks, and self-evolving reinforcement learning frameworks. These developments are not only advancing the practical applications of LLMs in software engineering but also setting new standards for performance and accessibility.

Noteworthy Developments:

  • InterTrans significantly improves code translation accuracy by leveraging intermediate programming languages.
  • MdEval introduces a comprehensive multilingual debugging benchmark, highlighting performance gaps between open-source and proprietary models.
  • WebRL transforms open LLMs into proficient web agents, outperforming proprietary models in web-based tasks.

Sources

Mastering the Craft of Data Synthesis for CodeLLMs

InterTrans: Leveraging Transitive Intermediate Translations to Enhance LLM-based Code Translation

MdEval: Massively Multilingual Code Debugging

WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning

Crystal: Illuminating LLM Abilities on Language and Code

CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models

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