Hallucination as a Latent Space Signal: Knowledge Boundary Detection and Cross-Model Routing via Latent Vectors

Key Points

• The work aims to reinterpret LLM hallucination as a signal indicating boundaries of knowledge rather than simply an error.
• Introduced the concept of hallucination as a 'gradient of ignorance'.
• Proposed an experimental protocol for boundary detection.
• Outlined approach for cross-model routing using latent vectors.
• Proposes a new framework for understanding hallucinations in LLMs.
• Suggests potential for improved model routing based on latent space signals.

Abstract