Query Understanding involves interpreting user queries to improve information retrieval. Reformulation rewrites queries for clarity or intent, decomposition breaks complex queries into simpler sub-queries, and expansion adds related terms to enhance results. In Retrieval-Augmented Generation (RAG), these techniques help models fetch relevant external data, ensuring more accurate and contextually appropriate responses by aligning user intent with retrieved information and generated answers.
Query Understanding involves interpreting user queries to improve information retrieval. Reformulation rewrites queries for clarity or intent, decomposition breaks complex queries into simpler sub-queries, and expansion adds related terms to enhance results. In Retrieval-Augmented Generation (RAG), these techniques help models fetch relevant external data, ensuring more accurate and contextually appropriate responses by aligning user intent with retrieved information and generated answers.
What is query reformulation?
Rewriting a user's query to improve clarity and alignment with a system's indexing and capabilities, often by fixing typos, normalizing terms, and resolving ambiguity.
What is query decomposition?
Breaking a complex query into smaller, solvable parts, answering each part separately, and then combining results to address the original question.
What is query expansion?
Adding related terms, synonyms, or related concepts to a query to retrieve more results, increasing recall, though it can reduce precision.
How do reformulation, decomposition, and expansion differ?
Reformulation changes the wording, decomposition splits a query into parts, and expansion broadens the query with additional terms to cover more possibilities.