PrivacyTransparencyAccountabilitycs.CL
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, Harm de Vries
May 9, 2023
Trans. Mach. Learn. Res.
1.4K citations
124 influential
Key Takeaways
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Responsible development requires transparency and developer consent: The StarCoder team built their training dataset (The Stack) from permissively licensed GitHub repositories and included an opt-out process, demonstrating that open-source AI models can be developed with explicit consideration for creators' rights and preferences.
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Safety measures are essential for public AI releases: The researchers implemented an improved PII (personally identifiable information) redaction pipeline and created a novel attribution tracing tool before releasing the model, showing that responsible AI deployment includes proactive steps to protect privacy and enable accountability.
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Open-source code models can match commercial alternatives: StarCoderBase outperformed OpenAI's code-cushman-001 model across multiple programming languages, suggesting that transparent, community-driven development can produce competitive AI systems without requiring proprietary approaches.
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Comprehensive evaluation strengthens trustworthiness: The paper includes "the most comprehensive evaluation of Code LLMs to date," which allows independent verification of the model's capabilities and limitations—a key practice for responsible AI that enables users to make informed decisions about deployment.
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Licensing matters for responsible AI access: The models were released under an improved version of the Open Responsible AI Model license designed to be "more commercially viable," balancing open access with protections that encourage responsible use across different organizational contexts.