FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, long

Summary

Factorized Linear Projection (FLiP) models are introduced for understanding and interpreting pretrained sentence embedding spaces. These models recover lexical content from multilingual (LaBSE), multimodal (SONAR), and API-based (Gemini) sentence embeddings across several high- and mid-resource languages. FLiP demonstrates the ability to recall over 75% of lexical content from embeddings, significantly outperforming existing non-factorized baselines and proving more effective and parameter-efficient than SpLiCE. Used as a diagnostic tool, FLiP uncovers modality and language biases within these encoders. The analysis reveals robust intra-language cross-modal alignment but highlights a strong English bias in cross-lingual representations, with semantic linearity degrading for linguistically distant languages. A 512-dimensional FLiP model achieves 76.77% accuracy on text, showing marginal performance degradation compared to its 1024-dimensional counterpart with half the parameters. The implementation is publicly available.

Key takeaway

For machine learning engineers evaluating or developing multimodal and multilingual sentence embedding models, FLiP provides a powerful intrinsic diagnostic tool. You should integrate FLiP into your model analysis workflow to uncover modality and language biases without relying solely on conventional downstream tasks. This allows you to identify and address the observed English-centric biases in cross-lingual representations, leading to more robust and globally applicable embedding models. Consider removing the bias term in keyword extraction for improved named entity recall.

Key insights

FLiP models effectively interpret sentence embeddings by linearly extracting lexical content, revealing underlying biases.

Principles

• Semantic concepts are linearly represented in well-encoded embedding spaces.
• Factorization of the vocabulary matrix is crucial for optimal keyword extraction performance.
• Cross-lingual embedding spaces often exhibit a strong English language bias.

Method

FLiP interprets embeddings via a keyword extraction proxy task using a factorized log-linear projection, optimized to maximize regularized log-likelihood, incorporating cross-modal/cross-lingual signals.

In practice

• Use FLiP as a diagnostic tool to analyze modality and language biases in sentence encoders.
• Apply $L_1$ regularization on the word embedding matrix for sparsity in keyword extraction.
• Remove the bias term in keyword extraction to improve named entity recall.

Topics

• Sentence Embeddings
• Model Interpretability
• Multimodal Embeddings
• Multilingual NLP
• FLiP Model
• Keyword Extraction

Code references

• BUTSpeechFIT/FLiP

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.