Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders (2505.05111v2)

Abstract: The mechanisms behind multilingual capabilities in LLMs have been examined using neuron-based or internal-activation-based methods. However, these methods often face challenges such as superposition and layer-wise activation variance, which limit their reliability. Sparse Autoencoders (SAEs) offer a more nuanced analysis by decomposing the activations of LLMs into a sparse linear combination of SAE features. We introduce a novel metric to assess the monolinguality of features obtained from SAEs, discovering that some features are strongly related to specific languages. Additionally, we show that ablating these SAE features only significantly reduces abilities in one language of LLMs, leaving others almost unaffected. Interestingly, we find some languages have multiple synergistic SAE features, and ablating them together yields greater improvement than ablating individually. Moreover, we leverage these SAE-derived language-specific features to enhance steering vectors, achieving control over the language generated by LLMs. The code is publicly available at https://github.com/Aatrox103/multilingual-LLM-features.

• The paper introduces a novel metric and method using sparse autoencoders to identify and measure language-specific features within large language models.
• Ablating these identified features specifically impacts performance in the associated language, demonstrating their localized importance for multilingual capabilities.
• Utilizing language-specific features allows for practical applications like enhancing steering vectors for more controlled language generation.

Unveiling Language-Specific Features in LLMs Using Sparse Autoencoders: Insights and Implications

This paper investigates the multilingual capabilities of LLMs through an innovative approach that leverages Sparse Autoencoders (SAEs) to extract and analyze language-specific features. Traditional methodologies, which include neuron-based and internal-activation-based techniques, have been limited by issues such as superposition and layer-wise activation variance. Here, SAEs present a promising alternative by offering a sparse linear decomposition of activations into interpretable feature directions.

Key Findings and Contributions

1. Monolinguality Metric Development: The paper introduces a metric designed to gauge the monolinguality of SAE-derived features. It is discovered that certain features have a distinct association with specific languages, effectively narrowing down the parts of LLMs that are language-unique.
2. Language-Specific Feature Ablation: Through directional ablation, the researchers demonstrate that removing these language-specific features significantly impacts the model's performance in the associated language, while sparing capabilities in others. This highlights the localized importance of these features in maintaining multilingual proficiency.
3. Influence beyond Specific Tokens: The analysis extends beyond language-specific tokens, exploring the role of linguistic context. This is observed through code-switching experiments, where embedding words within a language-specific context modulates feature activation, indicating that multilingual capabilities are not merely tied to vocabulary but also context.
4. Synergistic Feature Interaction: The paper finds that simultaneous ablation of multiple language-specific features results in a more pronounced decline in language-specific performance compared to individual ablations. This implies a synergistic interaction among language features within LLMs.
5. Enhanced Steering Vectors: Utilizing language-specific features to guide steering vectors, the researchers achieve improved control over language usage in generated outputs. This advancement offers practical benefits in fine-tuning LLMs for specific multilingual applications.

Theoretical and Practical Implications

Theoretical Implications: This work significantly advances understanding of how LLMs internally manage multilingual data. By decomposing model activations into language-specific features, the insights can direct future model architecture modifications to enhance multilingual capabilities.

Practical Implications: The findings offer practical methods for better tuning LLMs in multilingual environments. The enhanced steering vectors could lead to more robust LLMs capable of precise language differentiation and adaptation, offering improved performance in translation and multilingual comprehension tasks.

Prospects for Future Research

The results underscore the potential of applying sparse autoencoders in deeper explorations of LLM internals. Future research could investigate the applicability of these techniques to lower-resource languages or develop more granified metrics and methodologies to distinguish and enhance lesser-understood language features.

Overall, the paper provides a solid foundation for leveraging sparse representations in understanding and improving the multilingual proficiency of LLMs, marking a significant step forward in computational linguistics and model interpretability.