The Capacity for Moral Self-Correction in Large Language Models

Original Paper: https://arxiv.org/abs/2302.07459

By: Deep Ganguli, Amanda Askell, Nicholas Schiefer, Thomas I. Liao, Kamilė Lukošiūtė, Anna Chen, Anna Goldie, Azalia Mirhoseini, Catherine Olsson, Danny Hernandez, Dawn Drain, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jackson Kernion, Jamie Kerr, Jared Mueller, Joshua Landau, Kamal Ndousse, Karina Nguyen, Liane Lovitt, Michael Sellitto, Nelson Elhage, Noemi Mercado, Nova DasSarma, Oliver Rausch, Robert Lasenby, Robin Larson, Sam Ringer, Sandipan Kundu, Saurav Kadavath, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Christopher Olah, Jack Clark, Samuel R. Bowman, Jared Kaplan

Abstract:

We test the hypothesis that language models trained with reinforcement learning from human feedback (RLHF) have the capability to "morally self-correct" -- to avoid producing harmful outputs -- if instructed to do so. We find strong evidence in support of this hypothesis across three different experiments, each of which reveal different facets of moral self-correction. We find that the capability for moral self-correction emerges at 22B model parameters, and typically improves with increasing model size and RLHF training. We believe that at this level of scale, language models obtain two capabilities that they can use for moral self-correction: (1) they can follow instructions and (2) they can learn complex normative concepts of harm like stereotyping, bias, and discrimination. As such, they can follow instructions to avoid certain kinds of morally harmful outputs. We believe our results are cause for cautious optimism regarding the ability to train language models to abide by ethical principles.

Summary Notes

Navigating Ethics in AI: How to Train Ethical Large Language Models

In the exciting world of artificial intelligence, large language models (LLMs) are leading the charge, pushing the boundaries of what machines can do.

However, the immense capabilities of LLMs bring up important ethical considerations. Groundbreaking research by Anthropic points towards a new direction in AI development, showing that LLMs can be taught to self-correct morally, minimizing harmful outputs.

The Promise of Ethical AI

This significant study reveals a key insight: LLMs, especially those with over 22 billion parameters, can naturally align with ethical norms and decrease bias.

This is due to their advanced understanding abilities and exposure to a wide range of training data. The question then becomes, how can we direct this potential towards creating AI systems that are not only proficient but also ethically sound?

Insights into AI’s Ethical Capabilities

Anthropic's research utilized a range of tests to assess LLMs' ethical self-correction abilities:

• Bias Benchmark for Question Answering (BBQ): Results showed LLMs could reduce stereotype bias when properly prompted, with larger models making greater improvements.
• Winogender Benchmark: This test evaluated occupational gender bias, finding that LLMs could reflect real demographic statistics or avoid gender stereotypes based on the instructions given.
• Discrimination in Admissions: In examining racial discrimination, it was found that LLMs could be neutral or favor historically disadvantaged groups, based on their programming.

These experiments highlight LLMs' adaptability to ethical considerations when guided correctly.

Tips for AI Engineers

For AI engineers aiming for ethical AI development, here are some actionable tips:

• Embed Ethical Guidelines Early: Start the AI development process with ethical guidelines in mind, ensuring LLMs learn from diverse and fair data.
• Use Prompt-Based Interventions: Use prompts to steer LLMs away from bias and harmful outputs. Tailored prompts can significantly improve ethical performance.
• Focus on Continuous Learning: AI should continuously learn and adapt, improving its ethical decision-making over time.
• Maintain Transparency and Accountability: Keep AI operations transparent and hold LLMs accountable for their outputs, adjusting models as necessary and being open about training methodologies and data.

The Future: A Collective Ethical AI Effort

Anthropic's research serves as a call to action for the AI community to prioritize ethics in LLM development and deployment. As AI engineers and technologists, we have the responsibility to lead AI towards benefiting society positively.

By adopting principles of ethical alignment and moral self-correction, we can ensure LLMs contribute positively to our world, upholding societal values.

The path to ethical AI is a group journey, requiring the tech community's united effort to navigate successfully. Let's commit to developing AI systems that are not only smart and efficient but also ethical and fair.