How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordings
arXiv:2606.00356v1 Announce Type: new Abstract: Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary interface for understanding what each feature represents. We ask whether these labels generalize: does a feature labeled for a concept actually track that concept across languages and scripts? Using Serbian digraphia as a controlled testbed -- the same language written in both Latin and Cyrillic via deterministic transliteration -- we first find that SAE feature sets activated by the same content
The proliferation of sparse autoencoders for interpreting language models necessitates understanding the robustness of their auto-generated labels, especially as AI models become more linguistically diverse.
This research provides crucial insights into the reliability and generalization of AI interpretation tools, directly impacting the development and trustworthiness of advanced AI systems across languages and scripts.
Our understanding of how well AI's internal representations (features) generalize across different linguistic contexts and writing systems is enhanced, informing future model design and evaluation.
- · AI researchers
- · Multilingual AI developers
- · AI ethics and safety organizations
- · Developers relying on unvalidated auto-interpretation
- · Companies with solely English-centric AI interpretations
Improved methods for evaluating and ensuring the cross-lingual generalization of AI model features will emerge.
This will lead to more robust and culturally nuanced AI agents and language models capable of operating effectively in diverse global contexts.
Enhanced trust and adoption of AI in non-English speaking markets could accelerate due to more interpretable and reliable cross-lingual AI systems.
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