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Original Paper
Original Paper: https://arxiv.org/abs/2212.08073
By: Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller, Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt, Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma, Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec, Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly, Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Jared Kaplan
Abstract:
As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.
Summary Notes
Simplified Blog Post: Introducing Constitutional AI - A Step Towards Safer AI
In the fast-paced world of artificial intelligence (AI), it's vital to create AI systems that are smart, safe, and ethically sound.
Constitutional AI (CAI) is a groundbreaking approach that uses a set of guiding principles, much like a constitution, to direct the behavior of AI systems.
This strategy aims to make AI systems helpful, honest, and harmless, while also cutting down on the need for human oversight during AI training.
Why We Need Constitutional AI
Adopting a constitutional approach to AI training addresses several challenges:
- Handling Complexity: As AI systems grow more complex, it's becoming harder to monitor them closely. CAI introduces a way for AI to self-supervise, improving efficiency.
- Safe Assistance: The objective is to build AI that can tackle sensitive or difficult questions by providing clear, useful answers, thus being safe without being unhelpful.
- Clear and Trustworthy: By embedding training goals in easy-to-understand principles, CAI makes AI decisions more transparent and trustworthy.
How It Works
CAI involves two main steps:
- Supervised Learning: Here, AI evaluates and adjusts its responses based on the set principles, refining its answers to align with ethical standards.
- Reinforcement Learning: In this phase, AI uses feedback generated by itself to further refine its behavior according to the principles, without needing human input.
Proven Success
Evidence shows CAI's effectiveness in making AI less harmful while keeping it helpful and truthful. Highlights include:
- A clear preference for CAI-trained models over traditional ones, showing better safety and performance.
- Use of chain-of-thought reasoning in training, helping the AI to reason transparently and justify its actions.
Contributions to AI
CAI offers valuable advancements:
- A scalable, efficient way to make AI safer and more helpful with less human oversight.
- It highlights the role of AI in supervising itself and the importance of explicit reasoning in making AI more ethically aligned.
- It encourages further research into self-supervising AI and the wider use of CAI in different areas of AI.
Future Directions
CAI's success opens up new possibilities for self-supervising AI systems that operate without human-labeled data.
This could transform various AI fields by ensuring AI strictly follows guidelines based on societal values and ethics.
Conclusion: The Importance of Constitutional AI
Constitutional AI is a significant breakthrough in developing AI systems that are autonomous and ethically guided.
By reducing reliance on human data and making AI decisions clearer, CAI sets a new standard for ethical AI training and deployment.
As AI becomes more integrated into our lives, CAI's principles and methods will be key in creating AI that is both intelligent and in line with our ethical values.
How Athina AI can help
Athina AI is a full-stack LLM observability and evaluation platform for LLM developers to monitor, evaluate and manage their models
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