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Gemma 2 and 3 translation features show limited cross-lingual transfer

By PulseAugur Editorial · [1 sources] ·

A new research paper investigates the cross-lingual validity of Sparse Autoencoder (SAE) features in Google's Gemma 2 and Gemma 3 language models. The study found that while many features appear frequently across different languages, they often have minimal or inconsistent causal effects on translation tasks. However, one specific feature in both Gemma 2 and Gemma 3 consistently improved translation quality as measured by COMET scores when amplified, and degraded it when ablated, suggesting a language-agnostic translation direction. AI

IMPACT Highlights limitations in current methods for interpreting LLM features across languages, suggesting a need for more robust validation techniques.

RANK_REASON Research paper published on arXiv detailing findings about language model features. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Gemma 2 and 3 translation features show limited cross-lingual transfer

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Research paper published on arXiv detailing findings about language model features. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Giang Son Nguyen, Nhi Ngoc-Yen Nguyen, Wray Buntine, Dung D. Le ·

    Recurrence Is Not Enough: Causally Validating Multilingual SAE Translation Features in Gemma 2 and 3

    arXiv:2609.04808v1 Announce Type: cross Abstract: Sparse autoencoder (SAE) features are increasingly used to explain and steer language-model behavior, but it remains unclear whether a feature found in one language context plays the same causal role when processing prompts in ano…

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