aiqbench analysis
This study provides mechanistic evidence that multilingual LLMs encode informal register as a portable, language-agnostic abstraction rather than isolated lexical memorizations. By isolating this shared core within the model's internal representations, researchers demonstrate the ability to causally steer output formality across diverse languages and scripts without retraining. The findings advance our understanding of how large models generalize pragmatic concepts beyond surface-level heuristics.
A Universal Vibe? Finding and Controlling Language-Agnostic Informal Register with SAEs
arXiv:2603.26236v2 Announce Type: replace Abstract: While multilingual language models successfully transfer factual and syntactic knowledge across languages, it remains unclear whether they process culture-specific pragmatic registers, such as slang, as isolated language-specific memorizations or as unified, abstract concepts. We study this by probing the internal representations of Gemma-2-9B-IT using Sparse Autoencoders (SAEs) across three typologically diverse source languages: English, Hebrew, and Russian. To definitively isolate pragmatic register processing from trivial lexical sensitivity, we introduce a novel dataset in which every target term is polysemous, appearing in both literal and informal contexts. We find that while much of the informal-register signal is distributed across language-specific features, a small but highly robust cross-linguistic core consistently emerges. This shared core forms a geometrically coherent ``informal register subspace'' that sharpens in the model's deeper layers. Crucially, these shared representations are not merely correlational: activation steering with these features causally shifts output formality across all source languages and transfers zero-shot to six unseen languages spanning diverse language families and scripts. Together, these results provide the first mechanistic evidence that multilingual LLMs internalize informal register not just as surface-level heuristics, but as a portable, language-agnostic pragmatic abstraction.
Title:A Universal Vibe? Finding and Controlling Language-Agnostic Informal Register with SAEs
Abstract:While multilingual language models successfully transfer factual and syntactic knowledge across languages, it remains unclear whether they process culture-specific pragmatic registers, such as slang, as isolated language-specific memorizations or as unified, abstract concepts. We study this by probing the internal representations of Gemma-2-9B-IT using Sparse Autoencoders (SAEs) across three typologically diverse source languages: English, Hebrew, and Russian. To definitively isolate pragmatic register processing from trivial lexical sensitivity, we introduce a novel dataset in which every target term is polysemous, appearing in both literal and informal contexts. We find that while much of the informal-register signal is distributed across language-specific features, a small but highly robust cross-linguistic core consistently emerges. This shared core forms a geometrically coherent ``informal register subspace'' that sharpens in the model's deeper layers. Crucially, these shared representations are not merely correlational: activation steering with these features causally shifts output formality across all source languages and transfers zero-shot to six unseen languages spanning diverse language families and scripts. Together, these results provide the first mechanistic evidence that multilingual LLMs internalize informal register not just as surface-level heuristics, but as a portable, language-agnostic pragmatic abstraction.
Camera-ready version. Accepted at EMNLP 2026
Computation and Language (cs.CL)
arXiv:2603.26236 [cs.CL]
(or arXiv:2603.26236v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2603.26236 Focus to learn more arXiv-issued DOI via DataCite
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