Aamir Shakir, founder of mixedbread.ai, is an expert in crafting advanced embedding and reranking models for search applications. He discusses the transformative power of rerankers in retrieval systems, emphasizing their role in enhancing search relevance and performance without complete overhauls. Aamir highlights the benefits of late interaction models like ColBERT for better interpretability and shares creative applications of rerankers beyond traditional use. He also navigates future challenges in multimodal data management and the exciting possibilities of compound models for unified search.
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insights INSIGHT
How Rerankers Score Relevance
Rerankers compare a query against candidate documents using token-level embeddings and a max-sim aggregation.
They produce a per-document relevance score used with thresholds to filter results.
volunteer_activism ADVICE
Use Late Interaction For Explainability
Prefer late-interaction models like ColBERT for interpretability because they reveal token-level matches and failure cases.
Expect higher storage and compute costs and plan optimizations for scale.
volunteer_activism ADVICE
Compress Token Embeddings To Scale
Reduce token-vector size and precision to cut storage and speed up late-interaction search.
Use int8 or even binary representations and optimized distance ops like Hamming where acceptable.
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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.
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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