Using RAG expansion to improve model speed and accuracy

Summary

In this episode of 'Real Terms for AI,' Aja Hammerly and Jason Davenport dive into the concept of document and query expansion in information retrieval systems. They explain how expanding documents and queries helps address vocabulary mismatches, improve search results, and enhance latency performance in Retrieval-Augmented Generation (RAG) platforms. By using techniques like extractive and abstractive expansion, developers can refine their systems to better match user queries and provide faster, more accurate responses. The discussion covers practical applications and emphasizes choosing the right models based on specific use cases.

Takeaways

• 😀 Document and query expansion are key techniques used in information retrieval systems, improving search accuracy and performance.
• 😀 Document expansion involves modifying or rewording text within documents to enhance search results by addressing term mismatches.
• 😀 Query expansion works by altering or enriching user queries to improve their match with available document embeddings or database content.
• 😀 Vocabulary mismatches (e.g., different terms for the same concept) are common challenges in information retrieval, and expansion helps address these mismatches.
• 😀 In document expansion, methods like extractive and abstractive expansion are used to either reword sentences or summarize their meaning in different ways.
• 😀 Query expansion leverages similar methods to document expansion, improving the likelihood of finding the best match in response to a user’s query.
• 😀 Expansion techniques help reduce the computational burden of searching by broadening the information available at runtime, improving system latency.
• 😀 The combination of document and query expansion can help improve both the quality and speed of responses in large-scale systems like RAG (Retrieval-Augmented Generation).
• 😀 In a production environment, combining machine learning methods, LLMs, and expansion techniques optimizes a system's cost, performance, and quality.
• 😀 The right choice of expansion methods and models depends on the specific app, data, and use case, highlighting the importance of customizing solutions for different applications.

Q & A

What is document and query expansion, and why is it important in information retrieval?

Document and query expansion are methods used to improve the relevance and accuracy of information retrieval systems. Document expansion involves expanding the content of documents by generating semantically similar terms or rewording sentences. Query expansion improves user queries by generating additional terms or rephrasing the query to increase the chances of matching relevant documents, leading to better retrieval performance.

How does document expansion help with term or vocabulary mismatch?

Document expansion helps address term or vocabulary mismatch by generating alternative terms or rewording sentences in the documents to match different ways users may express their queries. For instance, if a document uses 'dictionaries' but a user queries 'hashmaps', document expansion can generate synonyms or related terms to improve search results.

Can semantic encoding and vector databases solve the vocabulary mismatch issue on their own?

Semantic encoding and vector databases help with understanding the meaning behind words, but they have limitations. Document expansion can further improve retrieval by adding synonyms or related terms, making it easier to match different ways users might phrase their queries, especially in systems that don't use vector databases.

How does document expansion impact system latency in information retrieval?

Document expansion can help reduce latency by pre-generating expanded content at runtime. This means the system doesn’t need to perform additional computational work when retrieving information. By expanding the available data beforehand, the retrieval process becomes faster and more efficient, which is crucial in high-performance systems.

What is the difference between extract expansion and abstractive expansion in document expansion?

Extract expansion involves directly generating different terms or phrases from the original document, while abstractive expansion attempts to create a new, reworded version of the content that captures the original meaning. Both methods are used to expand documents for better matching in retrieval systems.

How do document and query expansion improve retrieval-augmented generation (RAG) systems?

Both document and query expansion enhance RAG systems by improving the quality of the documents and queries used in the retrieval process. Document expansion increases the variety of terms and phrases in the database, while query expansion ensures that user queries are more likely to match the expanded documents, ultimately improving retrieval accuracy and system performance.

What role does document expansion play in reducing the need for complex computations during query retrieval?

Document expansion helps reduce the need for complex computations during query retrieval by expanding the document database with synonyms, reworded sentences, or abstracted content beforehand. This way, when a user query is made, the system can quickly find relevant documents without having to compute new matches on the fly, improving response time and efficiency.

What are some practical examples where document expansion would be beneficial?

Document expansion is beneficial in cases where users may ask questions with different wording or terminology than what is used in the original documents. For example, in a product manual, a user might search for 'fixing a broken ice maker', while the document might describe the same issue using terms like 'repairing the freezer.' Document expansion would help bridge that gap and provide better search results.

How does query expansion work in conjunction with document expansion in a retrieval system?

Query expansion works alongside document expansion by rephrasing or adding terms to the user's query to improve its chances of matching expanded documents. For instance, if a user asks a query like 'cost of refrigerator repair,' query expansion might add synonyms like 'price' or 'fee' to the query, increasing the likelihood of finding relevant documents that match the expanded query.

Can expansion methods like document and query expansion be used in multi-modal systems?

Yes, expansion methods can be used in multi-modal systems where different models handle different stages of the retrieval process. For example, a rewriter or query expander model might be used to enhance the input query, while a different model or LLM might handle the document retrieval and answer generation. These methods can work together to improve the overall performance of multi-modal and agentic systems.