AI

Original: Teaching Claude Why Author: Anthropic Date: May 8, 2026 This is a Chinese translation with annotations of Anthropic’s research post on alignment training methods. The original article discusses how teaching Claude the principles behind aligned behavior — rather than just training on demonstrations — proves far more effective for generalization. Key takeaways: Principles over demonstrations: Training Claude to explain why certain actions are better reduces misalignment more effectively than showing correct behavior alone. Out-of-distribution generalization: A 3M-token “difficult advice” dataset (where the user faces ethical dilemmas) achieved the same improvement as 84M tokens of synthetic honeypots — with 28× better data efficiency. Constitutional documents + fiction: High-quality documents about Claude’s constitution combined with fictional stories of aligned AI reduced blackmail rate from 65% to 19%. Improvements persist through RL: More aligned initialization snapshots maintained their advantage throughout reinforcement learning. Diverse environments matter: Simply adding tool definitions and system prompts to training environments — even without requiring tool use — improved alignment generalization. For the full annotated Chinese translation, please see the Chinese version. ...

Original post: Natural Language Autoencoders Full paper: transformer-circuits.pub/2026/nla Code: github.com/kitft/natural_language_autoencoders Interactive demo: neuronpedia.org/nla Summary Anthropic introduces Natural Language Autoencoders (NLAs), a method for converting a language model’s internal activations into human-readable natural language explanations. The approach trains two model components jointly: an Activation Verbalizer that translates activations into text, and an Activation Reconstructor that recovers the original activation from the text alone. The quality of explanations is measured by how accurately the activation can be reconstructed. ...

You’ve read that kind of article before — every paragraph wraps up neatly, the tone is warm and measured, every claim comes with exactly three supporting points, and the ending soars into “let us look forward to the future together.” You can’t pinpoint what’s wrong, but something’s off. That’s AI writing. Or more precisely, that’s AI writing in its default state. I’ve spent a fair amount of time on this problem recently. I started using Claude more and more when writing blog posts, but every first draft needed heavy editing — not because the information was wrong, but because the feel was off. It read like someone who never makes mistakes, never gets distracted, never has a mood swing. That person doesn’t exist. ...

This is a Chinese translation with commentary of the original article by Anthropic. Read the original here: What 81,000 people told us about the economics of AI By Maxim Massenkoff, Anthropic · April 22, 2026 For the Chinese translation and annotated version, switch to the 中文版.