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Guided by the Machine: A Framework for Mechanistic Interpretability in Language Models

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Yingjing Lu
Yingjing Lu

Guided by the Machine: A Framework for Mechanistic Interpretability in Language Models

Understanding how large language models (LLMs) process and represent information remains a significant challenge. In their paper Guided Mechanistic Interpretability: Reverse-Engineering Models with Machine-Guided Concept Discovery, Nebojsa Jojic and collaborators propose a framework that makes reverse-engineering LLM internals more systematic and scalable. Their approach uses guiding signals to align interpretable components of the model with human-understandable concepts, aiming to bridge the gap between neurons and abstract behaviors.

1. Motivation and Goals

Mechanistic interpretability aims to understand the internal mechanics of models — which neurons or attention heads are responsible for specific computations. However, existing approaches often rely on manual probing and intuition.

The authors propose a machine-guided framework that:

  • Automates the discovery of interpretable components.
  • Uses external guiding signals (e.g. grammars, policies) to anchor the model's behavior.
  • Identifies how and where behaviors are represented and manipulated inside LLMs.

2. The Guided Interpretability Framework

The key idea is to reverse-engineer the model’s internals using mechanistic hypotheses and optimization. The pipeline consists of:

  • Hypothesis Function: Defines a potential explanation of how the model might encode or compute a behavior.
  • Intervention Module: Tests the hypothesis by modifying internal representations (e.g., residual streams, activations).
  • Optimization Loop: Finds a linear combination of internal components (e.g., directions in residual stream) that best aligns with the guiding signal.

This approach lets the model help discover interpretable abstractions of its own computation, hence the term guided.

3. Applications and Case Studies

The authors demonstrate their method in multiple settings, including:

  • Natural Language Inference (NLI): They show that the model encodes entailment decisions in a linearly decodable direction in the residual stream, even across different architectures (Pythia, LLaMA).
  • Reinforcement Learning Agents: For transformer-based agents in gridworlds, they interpret how the model encodes policies and observations.
  • Symbolic Computation: Applying guided interpretability to models trained on grammar parsing tasks, revealing how the grammar rules are embedded internally.

Each case confirms that guided directions often correspond to meaningful, generalizable behaviors across tasks and instances.

4. Advantages Over Prior Interpretability Work

Compared to earlier work, this framework offers several benefits:

  • Scalability: The optimization-based design reduces manual effort and improves consistency across layers and models.
  • Generalizability: Works across modalities — text, environments, symbols — and across architectures.
  • Causal Testing: By actively intervening on internal states, it supports causal claims about what internal components do.

5. Limitations and Future Directions

While powerful, the framework assumes access to:

  • A good guiding signal (e.g., ground-truth labels, expert annotations).
  • Differentiable mechanisms for testing hypotheses, which may not always exist.
  • Interpretable and linearly-decodable structures (nonlinear mechanisms are harder to capture).

Future work could:

  • Extend to deeper, more entangled behaviors.
  • Integrate with human feedback for hybrid-guided interpretability.
  • Automate hypothesis generation using meta-learning.

6. Implications for Model Safety and Transparency

By enabling precise, causal insights into model computations, guided interpretability has clear implications:

  • Alignment: Ensures models behave as intended and encode ethical behavior appropriately.
  • Debugging: Helps identify failure points or misgeneralizations.
  • Auditing: Makes regulatory compliance and explainability more tractable.

References:

  • Jojic, N. et al. (2025). Guided Mechanistic Interpretability: Reverse-Engineering Models with Machine-Guided Concept Discovery. arXiv preprint arXiv:2505.06493. https://arxiv.org/abs/2505.06493