The Hidden Space of Transformer Language Adapters (2402.13137v2)

Abstract: We analyze the operation of transformer language adapters, which are small modules trained on top of a frozen LLM to adapt its predictions to new target languages. We show that adapted predictions mostly evolve in the source language the model was trained on, while the target language becomes pronounced only in the very last layers of the model. Moreover, the adaptation process is gradual and distributed across layers, where it is possible to skip small groups of adapters without decreasing adaptation performance. Last, we show that adapters operate on top of the model's frozen representation space while largely preserving its structure, rather than on an 'isolated' subspace. Our findings provide a deeper view into the adaptation process of LLMs to new languages, showcasing the constraints imposed on it by the underlying model and introduces practical implications to enhance its efficiency.

• The paper demonstrates that language adapters incrementally update predictions across layers, with final layers crucial for shifting towards target languages.
• It reveals that adapters leverage the preexisting model representation, preserving core structure while enabling gradual multilingual adjustments.
• Experimental results indicate that omitting non-critical adapter groups can reduce computational overhead without significantly impacting performance.

Investigating the Behavior of Language Adapters in Transformer Models

Introduction

The use of language adapters, small modules trained atop a static LLM (LM) to adjust its predictions for new target languages, has become a prevalent approach in adapting pre-trained LLMs for multilingual capabilities. Despite their widespread application, the specifics of how adapters function internally remain largely unexplored. This gap in understanding limits the potential for informed decisions regarding language selection for multilingual pre-training and crafting more efficient adaptation strategies. This research seeks to bridge this gap by providing insights into the internal workings of language adapters, specifically examining their impact on the evolution of LM predictions, the extent to which adaptations are distributed across the model's layers, and the underlying structural implications.

Key Findings

• Adapted predictions primarily evolve in the source language distribution throughout the inference process; only in the terminal layers does the target language prominently emerge.
• The adaptation process afforded by adapters is incremental and extends across most layers, with the potential to omit small groups of adapters without degrading performance significantly. However, adapters in the final layers prove critical for the successful adaptation towards target languages.
• Contrary to operating within an isolated subspace, adapters work atop the pre-existing structure of the LM’s representation space, maintaining its integrity while facilitating the gradual transition towards target language representations.
• Experimental results underscore the nuanced role of individual adapters across different languages, with adaptation requiring more nuanced adjustments for languages that are significantly distinct from the source language.

Implications for Research and Practice

The observations that adapters induce gradual, incremental updates in the LM's predictions, and that the adaptation process leverages the foundational representation structure of pre-trained models, have profound implications for the future development of language adaptation methodologies. Specifically, these insights suggest avenues for optimizing adapter-based approaches, potentially reducing the computational overhead involved in adapting LMs across multiple languages by identifying and focusing on the most impactful layers for adaptation.

Future Directions

Given the foundational nature of these findings, several promising research trajectories present themselves:

• Efficiency Optimization: Exploring strategies for identifying and selectively updating the most impactful adapters or layers could yield more computationally efficient approaches to language adaptation without significant losses in performance.
• Structural Analysis: Further analysis into the structural constraints imposed by the underlying pre-trained model on the adaptation process could lead to novel adaptation strategies that either circumvent or leverage these constraints more effectively.
• Beyond Language Adaptation: Investigating whether similar principles apply to other forms of model adaptation, such as domain adaptation, could broaden the applicability of these insights.

Conclusion

This paper provides vital insights into the internal operation of language adapters, showcasing the gradual evolution of adapted predictions across layers and affirming the preservation of the pre-trained model's representational structure. These findings not only enhance our understanding of the adaptation process but also open up new prospects for refining and optimizing the deployment of language adapters in multilingual LLMs. As the field continues to evolve, the principles uncovered in this research will likely play a central role in guiding the development of more effective and efficient adaptation methodologies.