Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation

Abstract:Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own activations. We introduce Universal Activation Verbalizer (UAV), a framework that uses a shared decoder to explain activations from heterogeneous donor models. UAV learns a lightweight adapter that converts donor activations into soft tokens in decoder's embedding space, and further supports adapter-only transfer by reusing a frozen decoder-side LoRA while training only a new adapter for another donor. Across classification, fact retrieval, and gist summarization, UAV remains competitive with strong self-explanation baselines while enabling cross-model verbalization across model families and scales. Ablations show that decoder-side tuning mainly improves task behavior, whereas the adapter provides the activation-grounded factual and semantic information needed for faithful explanations. Code and data are available at this https URL.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2605.25903 [cs.CL]

(or arXiv:2605.25903v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.25903 Focus to learn more arXiv-issued DOI via DataCite

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