Ethan Perez is a research scientist at Anthropic, working on large language models. He is the second Ethan working with large language models coming on the show but, in this episode, we discuss why alignment is actually what you need, not scale. We discuss three projects he has been pursuing before joining Anthropic, namely the Inverse Scaling Prize, Red Teaming Language Models with Language Models, and Training Language Models with Language Feedback.

OUTLINE

(00:00:00) Highlights

(00:00:20) Introduction

(00:01:41) The Inverse Scaling Prize

(00:06:20) The Inverse Scaling Hypothesis

(00:11:00) How To Submit A Solution

(00:20:00) Catastrophic Outcomes And Misalignment

(00:22:00) Submission Requirements

(00:27:16) Inner Alignment Is Not Out Of Distribution Generalization

(00:33:40) Detecting Deception With Inverse Scaling

(00:37:17) Reinforcement Learning From Human Feedback

(00:45:37) Training Language Models With Language Feedback

(00:52:38) How It Differs From InstructGPT

(00:56:57) Providing Information-Dense Feedback

(01:03:25) Why Use Language Feedback

(01:10:34) Red Teaming Language Models With Language Models

(01:20:17) The Classifier And Advesarial Training

(01:23:53) An Example Of Red-Teaming Failure

(01:27:47) Red Teaming Using Prompt Engineering

(01:32:58) Reinforcement Learning Methods

(01:41:53) Distributional Biases

(01:45:23) Chain of Thought Prompting

(01:49:52) Unlikelihood Training and KL Penalty

(01:52:50) Learning AI Alignment through the Inverse Scaling Prize

(01:59:33) Final thoughts on AI Alignment

Australia's Mixed Selection and Lyon Omission

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