# Kajal: Extracting Grammar of a Source Code Using Large Language Models

arXiv: https://arxiv.org/abs/2412.08842 · HTML: https://mlsanity.com/paper/2412.08842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://mlsanity.com/paper/2205.03401/) (2022)
- [Language Model Crossover: Variation through Few-Shot Prompting](https://mlsanity.com/paper/2302.12170/) (2023)

## Abstract (arXiv)

Understanding and extracting the grammar of a domain-specific language (DSL) is crucial for various software engineering tasks; however, manually creating these grammars is time-intensive and error-prone. This paper presents Kajal, a novel approach that automatically infers grammar from DSL code snippets by leveraging Large Language Models (LLMs) through prompt engineering and few-shot learning. Kajal dynamically constructs input prompts, using contextual information to guide the LLM in generating the corresponding grammars, which are iteratively refined through a feedback-driven approach. Our experiments show that Kajal achieves 60% accuracy with few-shot learning and 45% without it, demonstrating the significant impact of few-shot learning on the tool's effectiveness. This approach offers a promising solution for automating DSL grammar extraction, and future work will explore using smaller, open-source LLMs and testing on larger datasets to further validate Kajal's performance.