# Context Conquers Parameters: Outperforming Proprietary LLM in Commit Message Generation

arXiv: https://arxiv.org/abs/2408.02502 · HTML: https://mlsanity.com/paper/2408.02502/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2024.

## Built on by (most cited first, 6 of 6)

- [Enhancing LLM Performance Through Debate: An Empirical Study on Multi-Agent Debate for Coding Tasks](https://mlsanity.com/paper/2503.12029/) (2025)
- [Towards an Understanding of Context Utilization in Code Intelligence](https://mlsanity.com/paper/2504.08734/) (2025)
- [From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skills](https://mlsanity.com/paper/2607.01456/) (2026)
- [Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generation](https://mlsanity.com/paper/2506.22776/) (2025)
- [Prompt-Driven Code Summarization: A Systematic Literature Review](https://mlsanity.com/paper/2604.15385/) (2026)
- [Talking to Itself While Coding: What Makes Comments Help Code Generation?](https://mlsanity.com/paper/2609.09242/) (2026)

## Abstract (arXiv)

Commit messages provide descriptions of the modifications made in a commit using natural language, making them crucial for software maintenance and evolution. Recent developments in Large Language Models (LLMs) have led to their use in generating high-quality commit messages, such as the Omniscient Message Generator (OMG). This method employs GPT-4 to produce state-of-the-art commit messages. However, the use of proprietary LLMs like GPT-4 in coding tasks raises privacy and sustainability concerns, which may hinder their industrial adoption. Considering that open-source LLMs have achieved competitive performance in developer tasks such as compiler validation, this study investigates whether they can be used to generate commit messages that are comparable with OMG. Our experiments show that an open-source LLM can generate commit messages that are comparable to those produced by OMG. In addition, through a series of contextual refinements, we propose lOcal MessagE GenerAtor (OMEGA) , a CMG approach that uses a 4-bit quantized 8B open-source LLM. OMEGA produces state-of-the-art commit messages, surpassing the performance of GPT-4 in practitioners' preference.