hal2k

> TL;DR a few model providers are learning from private conversations across many organisations, giving their models better representations of how those organisations secure their systems and plausibly making semi-autonomous attacks easier. Private credentials are given directly to LLMs at scale. Beyond the same WiFi password being reused across office-wifis...

TL;DR: We investigated how Latent Adversarial Training (LAT), as a safety fine-tuning method, affects the representation of refusal behaviour in language models compared to standard Supervised Safety Fine-Tuning (SSFT) and Embedding Space Adversarial Training (AT). We found that LAT appears to encode refusal behaviour in a more distributed way across...