# Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach

arXiv: https://arxiv.org/abs/2412.00726 · HTML: https://mlsanity.com/paper/2412.00726/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 1 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Neural Turing Machines](https://mlsanity.com/paper/1410.5401/) (2014)
- [Latent Predictor Networks for Code Generation](https://mlsanity.com/paper/1603.06744/) (2016)
- [A parallel corpus of Python functions and documentation strings for automated code documentation and code generation](https://mlsanity.com/paper/1707.02275/) (2017)
- [Single-Pass Pivot Algorithm for Correlation Clustering. Keep it simple!](https://mlsanity.com/paper/2305.13560/) (2023)
- [On the Consistency of Maximum Likelihood Estimation of Probabilistic Principal Component Analysis](https://mlsanity.com/paper/2311.05046/) (2023)

## Built on by (most cited first, 10 of 10)

- [MM-PoE: Multiple Choice Reasoning via. Process of Elimination using Multi-Modal Models](https://mlsanity.com/paper/2412.07148/) (2024)
- [MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation](https://mlsanity.com/paper/2511.14967/) (2025)
- [Time-Constrained Recommendations: Reinforcement Learning Strategies for E-Commerce](https://mlsanity.com/paper/2512.13726/) (2025)
- [CLT and Edgeworth Expansion for m-out-of-n Bootstrap Estimators of The Studentized Median](https://mlsanity.com/paper/2505.11725/) (2025)
- [PixRec: Leveraging Visual Context for Next-Item Prediction in Sequential Recommendation](https://mlsanity.com/paper/2601.06458/) (2026)
- [Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study](https://mlsanity.com/paper/2604.12337/) (2026)
- [Single Conversation Methodology: A Human-Centered Protocol for AI-Assisted Software Development](https://mlsanity.com/paper/2507.12665/) (2025)
- [Is Sliding Window All You Need? An Open Framework for Long-Sequence Recommendation](https://mlsanity.com/paper/2604.12372/) (2026)
- [LLM-Based Code Documentation Generation and Multi-Judge Evaluation](https://mlsanity.com/paper/2606.09852/) (2026)
- [The Illusion of Agentic Complexity in README.md Generation: Evaluating Single-Agent vs. Multi-Agent RAG Systems](https://mlsanity.com/paper/2606.30524/) (2026)

## Abstract (arXiv)

Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use as a support tool to generate basic documentation for any publicly available repository. Over the last decade, several papers have been written on generating documentation for source code using neural network architectures. With the recent advancements in LLM technology, some open-source applications have been developed to address this problem. However, these applications typically rely on the OpenAI APIs, which incur substantial financial costs, particularly for large repositories. Moreover, none of these open-source applications offer a fine-tuned model or features to enable users to fine-tune. Additionally, finding suitable data for fine-tuning is often challenging. Our application addresses these issues which is available at https://pypi.org/project/readme-ready/.