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MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models

Summary

A new framework, MechELK, uses mechanistic interpretability to extract hidden knowledge from LLMs. It combines sparse autoencoders, causal probing, and representation engineering to achieve 84.7% accuracy, outperforming existing methods. The framework is particularly effective at surfacing knowledge when models give incorrect or evasive answers.

SourcearXiv Computational LinguisticsAuthor: Ji-jun Park, Soo-joon Choi, Jiwon Jeong, Taeyang Yoon, Ju-Wan Lee
MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models
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[2605.28825] MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models

[Submitted on 7 Apr 2026]

Title:MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models

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Abstract: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 elicitation. MechELK operates through: (1) \textbf{Locate} -- using Sparse Autoencoder (SAE) feature analysis and activation patching to identify knowledge-bearing representations; (2) \textbf{Verify} -- employing causal probing to distinguish genuine latent knowledge from spurious correlations; and (3) \textbf{Elicit} -- applying representation engineering to surface hidden knowledge without modifying model weights. Evaluated on TruthfulQA, a curated Deceptive Alignment benchmark, and the Quirky LM dataset, MechELK achieves an average elicitation accuracy of 84.7\%, outperforming CCS by 6.2\% and direct linear probing by 9.1\%. Crucially, MechELK successfully identifies latent knowledge in 78.3\% of cases where the model's surface output is incorrect or evasive, demonstrating its utility for AI safety applications including deceptive alignment detection.

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Computation and Language (cs.CL)

Cite as: arXiv:2605.28825 [cs.CL]

(or arXiv:2605.28825v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.28825

arXiv-issued DOI via DataCite

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From: Ji-Jun Park [view email] [v1] Tue, 7 Apr 2026 18:05:06 UTC (97 KB)

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Key points and analysis

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Key points

  • MechELK is a three-stage framework (Locate, Verify, Elicit) for extracting latent knowledge from LLMs.
  • It achieves an average elicitation accuracy of 84.7%, outperforming CCS and linear probing.
  • The method successfully identifies latent knowledge in 78.3% of cases where surface outputs are wrong.
  • It has implications for AI safety, including detecting deceptive alignment.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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