Why hypothetical document embeddings (HyDE)?

Traditional document retrieval in RAG models relies on matching queries with existing documents in a collection. This approach faces limitations:

  • Limited generalizability: Existing retrieval methods often struggle with unseen domains or queries with subtle variations.

  • Factual accuracy: Retrieving documents based solely on keyword matching might lead to irrelevant or inaccurate information, especially for complex queries.

HyDE tackles these challenges by introducing the concept of hypothetical documents.

Educative Byte: Assume you are a student and preparing for a history test with lots of books to read. HyDE, like a smart study buddy, jumps in to lend a hand. It takes all that information and makes super helpful study notes just for you. These notes aren’t copies of the books, but they’re the most important bits you need to remember. For instance, if you’re studying World War II, HyDE might summarize the big reasons for the war, the major battles, and how it ended. HyDE’s summaries make studying much easier—you can understand the main ideas faster.

What is HyDE?

HyDE, as described in thisGao, Luyu, Xueguang Ma, Jimmy Lin, and Jamie Callan. "Precise zero-shot dense retrieval without relevance labels." arXiv preprint arXiv:2212.10496 (2022). paper by Luyu Gao, leverages LLMs to generate hypothetical document embeddings that represent ideal documents for answering a given query. These embeddings, even though not corresponding to actual documents, capture the essence of the information needed. This allows the retrieval process to focus on documents containing relevant content, leading to more accurate and informative responses.

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