# Whose Text Is It Anyway? Exploring BigCode, Intellectual Property, and Ethics

arXiv: https://arxiv.org/abs/2304.02839 · HTML: https://mlsanity.com/paper/2304.02839/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 1 of 1)

- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)

## Built on by (most cited first, 3 of 3)

- [Traces of Memorisation in Large Language Models for Code](https://mlsanity.com/paper/2312.11658/) (2023)
- [LiCoEval: Evaluating LLMs on License Compliance in Code Generation](https://mlsanity.com/paper/2408.02487/) (2024)
- [How Much Do Code Language Models Remember? An Investigation on Data Extraction Attacks before and after Fine-tuning](https://mlsanity.com/paper/2501.17501/) (2025)

## Abstract (arXiv)

Intelligent or generative writing tools rely on large language models that recognize, summarize, translate, and predict content. This position paper probes the copyright interests of open data sets used to train large language models (LLMs). Our paper asks, how do LLMs trained on open data sets circumvent the copyright interests of the used data? We start by defining software copyright and tracing its history. We rely on GitHub Copilot as a modern case study challenging software copyright. Our conclusion outlines obstacles that generative writing assistants create for copyright, and offers a practical road map for copyright analysis for developers, software law experts, and general users to consider in the context of intelligent LLM-powered writing tools.