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Near-Duplicate Detection in Sycamore: What Is It Good For?

๐ŸŒˆ Abstract

The article discusses the implementation of near-duplicate detection (NDD) in Sycamore, a search engine, and how it can improve retrieval-augmented generation (RAG) by providing more unique and relevant documents as context for language models. The article covers the benefits of NDD in text retrieval and RAG, and provides examples of how NDD can enhance the quality of search results and RAG-generated summaries.

๐Ÿ™‹ Q&A

[01] Recently added near-duplicate-detection (NDD) support

1. What is the purpose of NDD in the context of Sycamore?

  • NDD is used to improve the relevance of search results by eliminating or grouping near-duplicate documents.
  • NDD can be applied at ingestion-time or query-time to address issues with relevance and recall that can arise from dropping near-duplicate documents during ingestion.

2. How does NDD work in Sycamore?

  • Sycamore uses a transform called Sketcher to generate a "sketch" (a set of numbers called a "shingle") for each document.
  • Near-duplicate documents are identified by comparing their sketches, and can be eliminated or grouped at query-time to improve the quality of the search results.

3. What are the benefits of performing NDD at query-time rather than ingestion-time?

  • Performing NDD at ingestion-time can lead to poor relevance, as potentially relevant documents may be dropped.
  • Query-time NDD allows the retrieval of all relevant documents, and then eliminates or groups near-duplicates in the result set, ensuring better coverage and relevance.

[02] Improving retrieval-augmented generation (RAG)

1. What is retrieval-augmented generation (RAG)?

  • RAG refers to the use of a large language model (LLM) to answer questions beyond the scope of what the LLM was trained on, by retrieving relevant documents from a search engine and using them as context for the LLM.

2. How can NDD improve RAG?

  • NDD can help fill the limited effective context size of RAG with a richer set of unique and relevant documents, leading to more comprehensive and informative answers generated by the LLM.
  • Examples are provided showing how NDD can improve the quality of RAG-generated summaries compared to using a non-deduped set of documents.

3. What are the consequences of not using NDD in a RAG pipeline?

  • Without NDD, the effective context size for the LLM may be limited to a smaller number of unique documents, resulting in less comprehensive and informative answers.
  • The examples demonstrate how NDD can significantly improve the quality and coverage of the RAG-generated summaries.
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