Today, we're talking to Aamir Shakir, the founder and baker at mixedbread.ai, where he's building some of the best embedding and re-ranking models out there. We go into the world of rerankers, looking at how they can classify, deduplicate documents, prioritize LLM outputs, and delve into models like ColBERT.

We discuss:

The role of rerankers in retrieval pipelines
Advantages of late interaction models like ColBERT for interpretability
Training rerankers vs. embedding models and their impact on performance
Incorporating metadata and context into rerankers for enhanced relevance
Creative applications of rerankers beyond traditional search
Challenges and future directions in the retrieval space

Still not sure whether to listen? Here are some teasers:

Rerankers can significantly boost your retrieval system's performance without overhauling your existing setup.
Late interaction models like ColBERT offer greater explainability by allowing token-level comparisons between queries and documents.
Training a reranker often yields a higher impact on retrieval performance than training an embedding model.
Incorporating metadata directly into rerankers enables nuanced search results based on factors like recency and pricing.
Rerankers aren't just for search—they can be used for zero-shot classification, deduplication, and prioritizing outputs from large language models.
The future of retrieval may involve compound models capable of handling multiple modalities, offering a more unified approach to search.

Aamir Shakir:

Nicolay Gerold:

00:00 Introduction and Overview 00:25 Understanding Rerankers 01:46 Maxsim and Token-Level Embeddings 02:40 Setting Thresholds and Similarity 03:19 Guest Introduction: Aamir Shakir 03:50 Training and Using Rerankers (Episode Start) 04:50 Challenges and Solutions in Reranking 08:03 Future of Retrieval and Recommendation 26:05 Multimodal Retrieval and Reranking 38:04 Conclusion and Takeaways

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