FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915945622011904 |
|---|---|
| author | Kesiraju, Santosh Yusuf, Bolaji Sedláček, Šimon Plchot, Oldřich Schwarz, Petr |
| author_facet | Kesiraju, Santosh Yusuf, Bolaji Sedláček, Šimon Plchot, Oldřich Schwarz, Petr |
| contents | 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 selected sentence encoders and provide practitioners with intrinsic insights about the encoders without relying on conventional downstream evaluation tasks. Our implementation is public https://github.com/BUTSpeechFIT/FLiP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18109 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings Kesiraju, Santosh Yusuf, Bolaji Sedláček, Šimon Plchot, Oldřich Schwarz, Petr Computation and Language Sound 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 selected sentence encoders and provide practitioners with intrinsic insights about the encoders without relying on conventional downstream evaluation tasks. Our implementation is public https://github.com/BUTSpeechFIT/FLiP. |
| title | FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings |
| topic | Computation and Language Sound |
| url | https://arxiv.org/abs/2604.18109 |