Abir Harrasse

Abir Harrasse, Florent Draye, Zhijing Jin et al.

Multilingual Large Language Models (LLMs) can process many languages, yet how they internally represent this diversity remains unclear. Do they form shared multilingual representations with language-specific decoding, and if so, why does performance still favor the dominant training language? To address this, we train a series of LLMs on different mixtures of multilingual data and analyze their internal mechanisms using cross-layer transcoders (CLT) and attribution graphs. Our results provide strong evidence for pivot language representations: the model employs nearly identical representations across languages, while language-specific decoding emerges in later layers. Attribution analyses reveal that decoding relies in part on a small set of high-frequency language features in the final layers, which linearly read out language identity from the first layers in the model. By intervening on these features, we can suppress one language and substitute another in the model's outputs. Finally, we study how the dominant training language influences these mechanisms across attribution graphs and decoding pathways. We argue that understanding this pivot-language mechanism is crucial for improving multilingual alignment in LLMs.

Narmeen Oozeer, Dhruv Nathawani, Nirmalendu Prakash et al.

The study of representation universality in AI models reveals growing convergence across domains, modalities, and architectures. However, the practical applications of representation universality remain largely unexplored. We bridge this gap by demonstrating that safety interventions can be transferred between models through learned mappings of their shared activation spaces. We demonstrate this approach on two well-established AI safety tasks: backdoor removal and refusal of harmful prompts, showing successful transfer of steering vectors that alter the models' outputs in a predictable way. Additionally, we propose a new task, \textit{corrupted capabilities}, where models are fine-tuned to embed knowledge tied to a backdoor. This tests their ability to separate useful skills from backdoors, reflecting real-world challenges. Extensive experiments across Llama, Qwen and Gemma model families show that our method enables using smaller models to efficiently align larger ones. Furthermore, we demonstrate that autoencoder mappings between base and fine-tuned models can serve as reliable ``lightweight safety switches", allowing dynamic toggling between model behaviors.