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Carlos Muñoz Ferrandis

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ICML Conference 2024 Conference Paper

Position: Standardization of Behavioral Use Clauses is Necessary for the Adoption of Responsible Licensing of AI

  • Daniel McDuff
  • Tim Korjakow
  • Scott Cambo
  • Jesse Josua Benjamin
  • Jenny Lee
  • Yacine Jernite
  • Carlos Muñoz Ferrandis
  • Aaron Gokaslan

Growing concerns over negligent or malicious uses of AI have increased the appetite for tools that help manage the risks of the technology. In 2018, licenses with behaviorial-use clauses (commonly referred to as Responsible AI Licenses) were proposed to give developers a framework for releasing AI assets while specifying their users to mitigate negative applications. As of the end of 2023, on the order of 40, 000 software and model repositories have adopted responsible AI licenses licenses. Notable models licensed with behavioral use clauses include BLOOM (language) and LLaMA2 (language), Stable Diffusion (image), and GRID (robotics). This paper explores why and how these licenses have been adopted, and why and how they have been adapted to fit particular use cases. We use a mixed-methods methodology of qualitative interviews, clustering of license clauses, and quantitative analysis of license adoption. Based on this evidence we take the position that responsible AI licenses need standardization to avoid confusing users or diluting their impact. At the same time, customization of behavioral restrictions is also appropriate in some contexts (e. g. , medical domains). We advocate for “standardized customization” that can meet users’ needs and can be supported via tooling.

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TMLR Journal 2023 Journal Article

StarCoder: may the source be with you!

  • Raymond Li
  • Loubna Ben allal
  • Yangtian Zi
  • Niklas Muennighoff
  • Denis Kocetkov
  • Chenghao Mou
  • Marc Marone
  • Christopher Akiki

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.

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TMLR Journal 2023 Journal Article

The Stack: 3 TB of permissively licensed source code

  • Denis Kocetkov
  • Raymond Li
  • Loubna Ben allal
  • Jia Li
  • Chenghao Mou
  • Yacine Jernite
  • Margaret Mitchell
  • Carlos Muñoz Ferrandis

Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language processing but also for code understanding and generation. To stimulate open and responsible research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. We describe how we collect the full dataset, construct a permissively licensed subset, present a data governance plan, discuss limitations, and show promising results on text2code benchmarks by training 350M-parameter decoders on different Python subsets. We find that (1) near-deduplicating the data significantly boosts performance across all experiments, and (2) it is possible to match previously reported HumanEval and MBPP performance using only permissively licensed data. We make the dataset available at https://hf.co/BigCode, provide a tool called "Am I in The Stack" for developers to search The Stack for copies of their code (https://hf.co/spaces/bigcode/in-the-stack), and provide a process for code to be removed from the dataset.

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