Embedding models for retrieval have long been treated as black boxes. That has started to change. A young research field at the intersection of mechanistic interpretability and information retrieval has begun opening retrievers up, and the findings quickly turn into capabilities: embeddings that unfold into interpretable latent terms, so you can actually read why a document matched; sparse features usable as an indexing vocabulary in their own right; and relevance signals you can locate and steer, changing what a retriever favours without retraining it. This talk is a tour of these findings and what they make possible in practice, including what the same lens reveals about classic failure modes such as hub documents and collapsing multi-intent queries.
Alper Nebi Kanlı is a Staff Software Engineer (AI Search) at Optimizely in London, where he builds search and retrieval systems. He has spent a decade working on search, AI engineering, and machine learning in production. He writes about language models at alpernebikanli.com, time to time speaks at London AI meetups including PyData London, and co-organizes the AI Signals meetup.
