Query Transformations: Rewriting, Step-back Prompting, and Sub-query Decomposition

Original Papers:

• Query Rewriting: https://arxiv.org/pdf/2305.14283
• Step-back Prompting: https://arxiv.org/abs/2310.06117
• Sub-query Decomposition: https://arxiv.org/pdf/2404.00610

Code Sample: https://github.com/NirDiamant/RAG_Techniques/blob/main/all_rag_techniques/query_transformations.ipynb

Query transformations are advanced techniques used to enhance the retrieval process in Retrieval-Augmented Generation (RAG) systems. These methods aim to improve the relevance and comprehensiveness of retrieved information by modifying or expanding the original query. Let's examine three key techniques in detail:

Query Rewriting

Query rewriting involves reformulating the original user query to improve its clarity, specificity, or relevance to the target information.

Process:

1. Analyze the original query
2. Identify key concepts and intent
3. Reformulate the query using techniques like:
• Adding synonyms or related terms
• Expanding abbreviations
• Clarifying ambiguous terms
• Incorporating domain-specific vocabulary

Advantages:

• Improves retrieval accuracy by aligning queries with document content
• Addresses issues of vocabulary mismatch between queries and documents
• Can adapt to specific domain terminology or jargon

Limitations:

• May introduce unintended bias or alter the original query intent if not carefully implemented
• Requires careful tuning to avoid over-expansion or drift from the original meaning
• Can be computationally expensive, especially for real-time applications

Step-back Prompting

Step-back prompting involves generating a more general or abstract version of the original query to capture broader context.

Process:

1. Analyze the original query
2. Identify the core topic or concept
3. Generate a "step-back" query that addresses a more general aspect of the topic
4. Use both the original and step-back queries for retrieval

Advantages:

• Captures broader context and background information
• Helps retrieve relevant information that may not directly match the specific query
• Particularly useful for complex or multi-faceted topics

Limitations:

• May retrieve overly general information if not balanced with the original query
• Can increase retrieval time and computational requirements
• Effectiveness depends on the ability to generate appropriate "step-back" queries

Sub-query Decomposition

Sub-query decomposition breaks down complex queries into simpler, more focused sub-queries.

Process:

1. Analyze the original query for multiple concepts or questions
2. Break the query into distinct sub-queries
3. Perform retrieval for each sub-query independently
4. Combine and synthesize the results

Advantages:

• Improves handling of complex, multi-part queries
• Allows for more targeted and diverse information retrieval
• Can improve coverage of all aspects of a complex question

Limitations:

• May increase overall retrieval time due to multiple searches
• Requires careful integration of results from different sub-queries
• Can be challenging to maintain coherence across sub-query results

General Considerations

When implementing these query transformation techniques, several factors should be considered:

Computational Overhead: All these methods can increase processing time and resource usage compared to simple keyword matching.

Balance with Original Query: It's crucial to maintain a balance between transformed queries and the original user intent to avoid drift or irrelevance.

Domain Adaptation: The effectiveness of these techniques can vary across different domains or types of information, requiring careful tuning and evaluation.

Integration with RAG Pipeline: These transformations need to be seamlessly integrated into the existing RAG workflow, considering factors like embedding generation and relevance scoring.

By carefully implementing and combining these query transformation techniques, RAG systems can significantly improve their ability to retrieve relevant and comprehensive information, leading to more accurate and contextually appropriate responses.

This is an AI generated summary.

Relevant Links:

1. https://mallahyari.github.io/rag-ebook/04_advanced_rag.html
2. https://www.datacamp.com/tutorial/how-to-improve-rag-performance-5-key-techniques-with-examples
3. https://www.promptingguide.ai/research/rag
4. https://www.6clicks.com/resources/blog/understanding-rag-retrieval-augmented-generation-explained
5. https://www.vantagediscovery.com/learning-center/what-is-retrieval-augmented-generation-rag
6. https://www.reddit.com/r/datascience/comments/16bja0s/why_is_retrieval_augmented_generation_rag_not/
7. https://stackoverflow.blog/2023/10/18/retrieval-augmented-generation-keeping-llms-relevant-and-current/
8. https://github.com/NirDiamant/RAG_Techniques/blob/main/all_rag_techniques/query_transformations.ipynb