Category: ML Research
Articles tagged ML Research
Early termination in HNSW for faster approximate KNN search
Learn how HNSW can be made faster for KNN search, using smart early termination strategies.
Understanding optimized scalar quantization
In this post, we explain a new form of scalar quantization we've developed at Elastic that achieves state-of-the-art accuracy for binary quantization.
cRank it up! - Introducing the Elastic Rerank model (in Technical Preview)
Get started in minutes with the Elastic Rerank model: powerful semantic search capabilities, with no required reindexing, provides flexibility and control over costs; high relevance, top performance, and efficiency for text search.
Exploring depth in a 'retrieve-and-rerank' pipeline
Select an optimal re-ranking depth for your model and dataset.
Introducing Elastic Rerank: Elastic's new semantic re-ranker model
Learn about how Elastic's new re-ranker model was trained and how it performs.
Better Binary Quantization (BBQ) vs. Product Quantization
Why we chose to spend time working on Better Binary Quantization (BBQ) instead of product quantization in Lucene and Elasticsearch.
What is semantic reranking and how to use it?
Introducing the concept of semantic reranking. Learn about the trade-offs using semantic reranking in search and RAG pipelines.
RaBitQ binary quantization 101
Understand the most critical components of RaBitQ binary quantization, how it works and its benefits. This guide also covers the math behind the quantization and examples.
Comparing ELSER for retrieval relevance on the Hugging Face MTEB Leaderboard
This blog compares ELSER for retrieval relevance on the Hugging Face MTEB Leaderboard.
Evaluating search relevance part 2 - Phi-3 as relevance judge
Using the Phi-3 language model as a search relevance judge, with tips & techniques to improve the agreement with human-generated annotation.
Evaluating search relevance part 1 - The BEIR benchmark
Learn to evaluate your search system in the context of better understanding the BEIR benchmark, with tips & techniques to improve your search evaluation processes.
