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claim:the-cl-auxiliary-loss-can-directly-reduce-representational-divergence-in-practical-interpretability-settings-without-sacrificing-interpretability-method-accuracy

The CL auxiliary loss can directly reduce representational divergence in practical interpretability settings without sacrificing interpretability method accuracy

Central practical contribution: the CL loss offers a viable mitigation strategy

Source paper

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Addressing divergent representations from causal interventions on neural networks

(2025) · Satchel Grant · Simon Jerome Han · Alexa R. Tartaglini · Christopher Potts

Neighborhood — ranked by edge-count

Findings (1)

finding

For small CL loss weights epsilon, IIA is maintained (potentially improved) while EMD decreases in Boundless DAS on a 7B LLM
associated_withsupports
Empirical result showing the CL loss can reduce divergence without sacrificing interpretability accuracy

Questions (1)

question

When it is not okay, how can we prevent divergent representations from occurring?
gates
Third core research question motivating the CL loss approach in Section 5

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 modified CL loss is confined to a narrow set of simplistic settings and is not specific to pernicious divergenceconcept0.797
Explicitly identified limitation of the proposed mitigation method
Counterfactual Latent (CL) Auxiliary Lossmethod0.758
Auxiliary objective combining L2 and cosine losses against pre-recorded CL vectors to improve causal relevance when one model is causally inaccessible.
Natural Language Autoencoders achieve readable explanations through unsupervised reconstruction loss optimized with reinforcement learning, not explicit interpretability constraints.claim0.756
Core insight: reconstruction objective combined with appropriate initialization and KL regularization produces human-interpretable explanations as emergent property.
Modified CL loss outperforms behavioral DAS loss in OOD transfer from dense to sparse class partitionfinding0.755
Key practical utility result: CL loss improves generalization of alignment to out-of-distribution settings
Causal abstraction is not enough for mechanistic interpretability because it becomes vacuous without assumptions about how models encode informationclaim0.747
Central thesis of the paper
Modified CL loss achieves IIA of 0.9988±0.0005 on synthetic 10-class dataset training/test setsfinding0.742
IIA for modified CL loss on synthetic dataset, comparable to behavioral DAS
Interpretable predictions can help resolve variants of uncertain significanceclaim0.740
Motivating claim that mechanistic explanations add clinical value for VUS.
Representational divergence (as measured by EMD) can predict lower out-of-distribution intervention performanceclaim0.731
Practical utility of reducing divergence demonstrated through regression analysis