Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability

This research explores using geometric deviation of LLM hidden states as a pre-generation signal to determine if a query is outside the model's knowledge, requiring no labeled failure data. Across various models and prompt forms, it finds that this signal effectively predicts unanswerable math prompts but not factual ones.