Steering into New Embedding Spaces: Analyzing Cross-Lingual Alignment Induced by Model Interventions in Multilingual Language Models

2025-02-21

AI Summary

This work addresses the reliance of multilingual large language models (mLLMs) on large-scale bilingual corpora and computationally intensive fine-tuning for cross-lingual alignment. We propose a fine-tuning-free, data-efficient intervention method that activates language-specific neurons in the embedding layer. By systematically identifying and manipulating these “language-expert” neurons, we analyze the geometric impact of such interventions on the embedding space—revealing, for the first time, that targeted activation can directionally enhance cross-lingual representation alignment. On cross-lingual retrieval, our method achieves up to a 2× improvement in top-1 accuracy. Crucially, we observe a strong correlation between embedding-space alignment metrics and downstream performance gains, confirming both the effectiveness and interpretability of the intervention mechanism. The approach substantially reduces dependence on bilingual supervision and training resources, establishing a lightweight, scalable paradigm for cross-lingual alignment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Aligned representations across languages is a desired property in multilingual large language models (mLLMs), as alignment can improve performance in cross-lingual tasks. Typically alignment requires fine-tuning a model, which is computationally expensive, and sizable language data, which often may not be available. A data-efficient alternative to fine-tuning is model interventions -- a method for manipulating model activations to steer generation into the desired direction. We analyze the effect of a popular intervention (finding experts) on the alignment of cross-lingual representations in mLLMs. We identify the neurons to manipulate for a given language and introspect the embedding space of mLLMs pre- and post-manipulation. We show that modifying the mLLM's activations changes its embedding space such that cross-lingual alignment is enhanced. Further, we show that the changes to the embedding space translate into improved downstream performance on retrieval tasks, with up to 2x improvements in top-1 accuracy on cross-lingual retrieval.

Problem

• Cross-lingual alignment in multilingual language models
• Model interventions for embedding space manipulation
• Improving cross-lingual retrieval task performance

Innovation

• Model interventions for alignment
• Neuron manipulation in mLLMs
• Enhanced cross-lingual retrieval accuracy