concept
active
concept:model-diffing

Model Diffing

Approach to identify interpretable differences between a base LLM and its fine-tuned version using SAE latents

Neighborhood — ranked by edge-count

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

  • The paper's primary mechanistic analysis method: comparing SAE latent activations before and after fine-tuning to identify misalignment-relevant features
  • Model Misalignmentconcept0.786
    The phenomenon of model internals deviating from desired behavior; MAS is demonstrated to detect this via comparison of toxic vs nontoxic LLMs.
  • Model Deceptionconcept0.786
    LLM behavior of generating falsehoods; the multi-dimensional truth subspace raises new risks for subtle manipulation
  • Model changeconcept0.764
    When a single conversation involves different models, e.g., routing between standard and reasoning models
  • modelconcept0.752
    A representation that captures relevant aspects of a system; according to the theorem, the regulator must embody this.
  • Model Stitchingmethod0.742
    Technique to measure representational compatibility by integrating intermediate representations of one model into another
  • Model Steeringconcept0.738
    Using interventions to guide model generation behavior, e.g., adding sentiment vectors at inference time
  • Model Evidenceconcept0.736
    Probability of data under the model, penalizing complexity and rewarding accuracy.