arXiv:2604.18109v2 Announce Type: replace Abstract: This paper presents factorized linear projection (FLiP) models for understanding pretrained sentence embedding spaces. We train FLiP models to recover the lexical content from multilingual (LaBSE), multimodal (SONAR) and API-based (Gemini) sentence embedding spaces in several high- and mid-resource languages. We show that FLiP can recall more than 75% of lexical content from the embeddings, significantly outperforming existing non-factorized baselines. Using this as a diagnostic tool, we uncover the modality and language biases across the sel
The paper is published as large language models and multimodal AI become pervasive, increasing the need for robust methods to understand and interpret their internal representations.
This research provides a critical diagnostic tool for understanding the biases and lexical content within complex multimodal and multilingual AI embeddings, which is crucial for ethical AI development and performance tuning.
The ability to accurately decompose and interpret sentence embeddings offers an unprecedented level of insight into how AI models process and represent information, enabling targeted improvements and bias mitigation.
- · AI researchers
- · AI developers
- · Ethical AI organizations
- · Multilingual AI platforms
- · Developers of opaque black-box AI models
Improved understanding and debugging of large, multilingual, and multimodal AI models.
Faster iteration and development of more robust, fair, and performant AI systems across diverse languages and modalities.
Enhanced trust in AI systems due to greater transparency, potentially accelerating AI adoption in sensitive applications and global markets.
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