MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models
Large language models (LLMs) frequently encode factual and reasoning knowledge in their internal representations that is not faithfully reflected in their surface-level outputs -- a phenomenon known as \emph{latent knowledge}. Existing approaches to eliciting latent knowledge, such as Contrastive Consistency Search (CCS), rely on contrastive activation patterns and struggle with complex multi-step reasoning tasks, while mechanistic interpretability tools have primarily been used to \emph{understand} model behavior rather than to \emph{extract} hidden knowledge. We present \textbf{MechELK}, a unified three-stage framework that bridges mechanistic interpretability and latent knowledge elici...
| Authors | Ji-jun Park, Soo-joon Choi, Jiwon Jeong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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