Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector
2026-06-29 • Computation and Language
Computation and LanguageArtificial IntelligenceMachine Learning
AI summaryⓘ
The authors studied special digital summaries called embeddings that combine different types of information into one. They focused on the SONAR model and found that some parts of these embeddings react differently when things go wrong, helping spot errors in decoding. By checking how well encoding and decoding match up, they made a tool that can detect these problems accurately. They also tried to fix the errors by adjusting specific parts of the embeddings. Their work highlights that understanding the embeddings themselves is important for making better multimodal systems.
multimodal embeddingssentence-level embeddingsSONAR modelencoding-decodingdecoding anomaliesembedding perturbationsdimensional analysiserror detectionrepresentation learningmultimodal representations
Authors
Elys Allesiardo, Antoine Caubrière, Valentin Vielzeuf
Abstract
This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model. We demonstrate that certain embedding dimensions are sensitive to perturbations and can serve as indicators of decoding anomalies. By leveraging the consistency between successive encoding and decoding, we successfully build an accurate detector. Additionally, we explore modifying specific dimensions of interest to attempt to correct them. This work underscores the importance of understanding and analyzing the embeddings themselves to enhance the reliability of multimodal representations.