Natural Language Autoencoders

Introduced Natural Language Autoencoders (NLAs), a novel method to convert LLM activations into human-readable text. NLAs consist of an Activation Verbalizer (AV) and an Activation Reconstructor (AR) trained in a round-trip fashion to ensure the textual explanation accurately reconstructs the original activation. Demonstrated NLAs' effectiveness in identifying hidden evaluation awareness in safety testing scenarios (e.g., 16% on destructive coding tasks, 26% on SWE-bench Verified) and in auditing misaligned models, where NLAs improved auditor win rates from <3% to 12-15%. Discussed limitations such as factual hallucinations and computational expense, and released code and an interactive demo.