Precise Zero-Shot Dense Retrieval without Relevance Labels (2212.10496v1)

Abstract: While dense retrieval has been shown effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no relevance label is available. In this paper, we recognize the difficulty of zero-shot learning and encoding relevance. Instead, we propose to pivot through Hypothetical Document Embeddings~(HyDE). Given a query, HyDE first zero-shot instructs an instruction-following LLM (e.g. InstructGPT) to generate a hypothetical document. The document captures relevance patterns but is unreal and may contain false details. Then, an unsupervised contrastively learned encoder~(e.g. Contriever) encodes the document into an embedding vector. This vector identifies a neighborhood in the corpus embedding space, where similar real documents are retrieved based on vector similarity. This second step ground the generated document to the actual corpus, with the encoder's dense bottleneck filtering out the incorrect details. Our experiments show that HyDE significantly outperforms the state-of-the-art unsupervised dense retriever Contriever and shows strong performance comparable to fine-tuned retrievers, across various tasks (e.g. web search, QA, fact verification) and languages~(e.g. sw, ko, ja).

• The paper introduces HyDE, a novel approach that generates hypothetical documents using instruction-following language models to enable zero-shot dense retrieval without relevance labels.
• It employs a two-step process where generated documents are transformed into embeddings by unsupervised contrastively learned encoders, bypassing traditional query-document matching.
• Experiments demonstrate that HyDE outperforms baseline unsupervised retrievers and rivals fine-tuned models, underscoring its practical and theoretical impact.

Exploring Zero-Shot Dense Retrieval with Hypothetical Document Embeddings (HyDE)

Introduction to HyDE

The emergence of dense retrieval methodologies has significantly advanced the efficiency and effectiveness of document retrieval across various languages and tasks. However, the development of fully zero-shot dense retrieval systems without the use of relevance labels remains a considerable challenge. This paper introduces a novel approach, named Hypothetical Document Embeddings (HyDE), which overcomes this hurdle by employing instruction-following LLMs to generate hypothetical documents that pivot the retrieval process.

The Concept Behind HyDE

HyDE decentralizes the traditional process of encoding relevance directly between a query and document. Instead, it innovates by splitting the retrieval task into two distinct phases:

• Generation of Hypothetical Documents: An instruction-following LLM generates a document based upon the query, encapsulating the relevant answer or information.
• Retrieval through Document Embedding: A contrastively learned encoder, not fine-tuned or specifically supervised for the current task, converts this generated document into an embedding vector. This representation is then utilized to locate and retrieve topically similar real documents from the corpus.

Methodology

The paper outlines a comprehensive methodology detailing the construction and operation of HyDE. Key components include:

• Instruction-following LLMs like InstructGPT used to generate hypothetical documents based on the query.
• Unsupervised contrastively learned encoders (e.g., Contriever) that create embedding vectors from the generated documents.
• A two-step process where relevance is modeled and captured through natural language generation, circumventing the need for explicit relevance scores or labels.

Empirical Evaluation

Extensive experiments showcased HyDE's effectiveness across a variety of tasks and languages including web search, QA, fact verification, and several low-resource settings. When compared to state-of-the-art unsupervised retrievers and fine-tuned models:

• HyDE consistently outperformed the baseline unsupervised dense retriever, demonstrating significant improvements in retrieval performance.
• The approach was competitive, and in some cases favorable, compared to fine-tuned dense retrievers, underscoring its potential practical utility and effectiveness in zero-shot settings.

Practical Implications and Theoretical Contributions

The proposed HyDE model opens new avenues for constructing dense retrieval systems without the requisite of relevance judgments, demonstrating the utility of leveraging generative capabilities for relevance modeling. Theoretically, it underscores the shifting paradigm towards employing natural language understanding and generation models for capturing document relevance, suggesting a potential reevaluation of numerical relevance scores in favor of language-driven approaches.

Concluding Thoughts and Future Directions

HyDE represents a significant step towards developing fully zero-shot dense retrieval systems capable of operating across a wide range of tasks and languages. It encourages further exploration into the role of natural language processing capabilities in retrieval tasks and poses intriguing questions about the nature of relevance and efficiency of retrieval systems sans labeled data. Future research might extend the concept to even more complex retrieval challenges, including multi-hop or conversational search.

In summary, HyDE not only presents a novel methodology for zero-shot dense retrieval but also prompts a reevaluation of existing retrieval paradigms, opening possibilities for future innovations in unsupervised or lightly supervised retrieval systems.