Seongwan Park

Decoding Dense Embeddings: Sparse Autoencoders for Interpreting and Discretizing Dense Retrieval

Despite their strong performance, Dense Passage Retrieval (DPR) models suffer from a lackof interpretability. In this work, we propose a novel interpretability framework that leveragesSparse Autoencoders (SAEs) to decompose previously uninterpretable dense embeddings fromDPR models into distinct, in