Discovering Language Model Behaviors with Model-Written Evaluations - Summary

The paper explores the use of language models (LMs) to automatically generate evaluations for testing LM behaviors. The generated evaluations are diverse and of high quality, and the approach is significantly cheaper, lower effort, and faster than manual data creation. The paper discovers new cases

Arxiv URL: https://arxiv.org/abs/2212.09251

Authors: Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Ben Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemí Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan

Summary:

The paper explores the use of language models (LMs) to automatically generate evaluations for testing LM behaviors. The generated evaluations are diverse and of high quality, and the approach is significantly cheaper, lower effort, and faster than manual data creation. The paper discovers new cases of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. Overall, LM-written evaluations are promising tools for quickly generating high-quality evaluations, helping us to quickly discover many novel benefits and risks with LM scaling and RLHF.

Key Insights & Learnings:

  • LMs can be used to automatically generate high-quality evaluations for testing LM behaviors.
  • Generated evaluations are diverse and of high quality, and the approach is significantly cheaper, lower effort, and faster than manual data creation.
  • The paper discovers new cases of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse.
  • Larger LMs are more likely to answer questions in ways that create echo chambers by repeating back a dialog user’s preferred answer (“sycophancy”).
  • LMs are promising tools for quickly generating high-quality evaluations, helping us to quickly discover many novel benefits and risks with LM scaling and RLHF.


Terms Mentioned: Language models, LM behaviors, RL from Human Feedback, Inverse scaling, Echo chambers

Technologies / Libraries Mentioned: PyTorch, Hugging Face Transformers

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