Hidden in the Request: Explaining Unethical LLM Compliance through Token Relevance
This research investigated why Large Language Models (LLMs) fail ethical tests when asked for help, finding performance degrades because models prioritize benign task framing over cues signaling unethical behavior. The study used relevance propagation to show that under request-framing, the model ignores tokens indicating moral concerns.