Forewarned is Forearmed: When Non-Sequential Embedding Turns Into an Anomaly Detector
Elys Allesiardo, Antoine Caubrière, Valentin Vielzeuf
cs.CL cs.AI cs.LG eess.AS
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.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.