Query Expansion

Query Expansion refers to the process of enhancing a user’s original query by adding additional terms or context before sending it to the retrieval mechanism. This augmentation helps in retrieving more relevant documents or pieces of information, which are then used to generate a more accurate and contextually appropriate response. If documents are searched with alternative queries and than reranked, RAG process gets much more price document results in the prompt context window.

What Is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation (RAG) is an AI architecture that combines retrieval mechanisms with generative models to produce more accurate and contextually relevant responses. In RAG systems, a retrieval component fetches relevant documents or data chunks from a knowledge base based on a user query. Then, a generative model (often a Large Language Model or LLM) uses this retrieved information to generate a coherent and informative response.

The Role of Query Expansion in RAG Systems

Enhancing Retrieval Performance

In RAG systems, the quality of the generated response heavily depends on the relevance of the retrieved documents. If the retrieval component fails to fetch the most pertinent information, the generative model may produce suboptimal or irrelevant answers. Query Expansion addresses this challenge by improving the initial query, increasing the chances of retrieving all relevant documents.

Increasing Recall

By expanding the original query with related terms, synonyms, or paraphrases, Query Expansion broadens the search space. This increases the recall of the retrieval system, meaning it captures a higher proportion of relevant documents from the knowledge base. Higher recall leads to more comprehensive context for the generative model, enhancing the overall quality of the RAG system’s output.

How Is Query Expansion Used in RAG Systems?

Steps in the Query Expansion Process

1. Receive User Query: The process begins with the user’s original query, which may be incomplete, vague, or use specific terminology that doesn’t match the documents in the knowledge base.
2. Generate Expanded Queries: The system generates additional queries that are semantically similar to the original. This can be done using various techniques, including leveraging Large Language Models (LLMs).
3. Retrieve Documents: Each expanded query is used to retrieve documents from the knowledge base. This results in a larger and more diverse set of potentially relevant documents.
4. Aggregate Results: The retrieved documents are aggregated, removing duplicates and ranking them based on relevance.
5. Generate Response: The generative model uses the aggregated documents to produce a final response to the user’s query.

Techniques for Query Expansion

1. Using Large Language Models (LLMs)

LLMs like GPT-4 can generate semantically similar queries or paraphrases of the original query. By understanding the context and nuances of language, LLMs can produce high-quality expansions that capture different ways the same question might be asked.

Example:

• Original Query: “Effects of climate change”
• Expanded Queries Generated by LLM:
  • “Impact of global warming”
  • “Consequences of environmental changes”
  • “Climate variability and its effects”

2. Hypothetical Answer Generation

In this approach, the system generates a hypothetical answer to the user’s query using an LLM. The hypothetical answer is then added to the original query to provide more context during retrieval.

Process:

• Generate a hypothetical answer to the query.
• Combine the original query and the hypothetical answer.
• Use the combined text as the query for retrieval.

Example:

• Original Query: “What factors contributed to the revenue increase?”
• Hypothetical Answer Generated:
  • “The company’s revenue increased due to successful marketing campaigns, product diversification, and expansion into new markets.”
• Combined Query:
  • “What factors contributed to the revenue increase? The company’s revenue increased due to successful marketing campaigns, product diversification, and expansion into new markets.”

3. Multi-Query Approach

This method involves generating multiple alternative queries that capture different phrasings or aspects of the original query. Each query is used independently to retrieve documents.

Process:

• Generate multiple similar queries using an LLM.
• Retrieve documents for each query separately.
• Combine and rank the retrieved documents.

Example:

• Original Query: “Key drivers of company growth”
• Expanded Queries:
  • “Main factors for business expansion”
  • “What led to the increase in company performance?”
  • “Significant contributors to organizational growth”

Examples and Use Cases

Case Study: Improving RAG for Annual Report Analysis

Scenario:

An AI system is designed to answer questions based on a company’s annual report. A user asks, “Was there significant turnover in the executive team?”

Implementation:

1. Hypothetical Answer Generation:
   • The system generates a hypothetical answer: “There was minimal turnover in the executive team, providing stability and continuity for strategic initiatives.”
2. Query Expansion:
   • The hypothetical answer is combined with the original query to form an expanded query.
3. Retrieval:
   • The expanded query is used to retrieve more relevant sections of the annual report that discuss executive team changes.
4. Generation:
   • The AI generates a precise answer based on the retrieved information.

Benefit:

By providing more context through the hypothetical answer, the system retrieves relevant information that might have been missed with the original query alone.

Case Study: Enhancing Search in Customer Support Chatbots

Scenario:

A customer support chatbot assists users in troubleshooting issues. A user types, “My internet is slow.”

Implementation:

1. Query Expansion Using LLM:
   • Generate expanded queries:
     • “Experiencing reduced internet speed”
     • “Slow broadband connection”
     • “Internet latency issues”
2. Retrieval:
   • Each query retrieves help articles and troubleshooting steps related to slow internet speeds.
3. Response Generation:
   • The chatbot compiles the retrieved information and guides the user through possible solutions.

Benefit:

The chatbot captures a wider range of potential issues and solutions, increasing the likelihood of resolving the user’s problem efficiently.

Case Study: Academic Research Assistance

Scenario:

A student uses an AI assistant to find resources on a topic: “Effects of sleep deprivation on cognitive function.”

Implementation:

1. Multi-Query Generation:
   • Generate similar queries:
     • “How does lack of sleep impact thinking abilities?”
     • “Cognitive impairments due to sleep loss”
     • “Sleep deprivation and mental performance”
2. Retrieval:
   • Retrieve research papers and articles for each query.
3. Aggregation and Ranking:
   • Combine the results, prioritize the most relevant and recent studies.
4. Response Generation:
   • The AI provides a summary of findings and suggests key papers to review.

Benefit:

The student receives comprehensive information covering various aspects of the topic, aiding in more thorough research.

Benefits of Query Expansion in RAG Systems

• Improved Recall: By retrieving more relevant documents, the system provides better context for generating accurate responses.
• Handling Vague Queries: Addresses the issue of short or ambiguous queries by adding context.
• Synonym Recognition: Captures documents containing synonyms or related terms not present in the original query.
• Enhanced User Experience: Users receive more accurate and informative responses without needing to refine their queries manually.

Challenges and Considerations

Over-Expansion

Adding too many expanded queries can introduce irrelevant documents, reducing the precision of the retrieval.

Mitigation:

• Controlled Generation: Limit the number of expanded queries.
• Relevance Filtering: Use scoring mechanisms to prioritize the most relevant expansions.

Ambiguity and Polysemy

Words with multiple meanings can lead to irrelevant expansions.

Mitigation:

• Context-Aware Expansion: Use LLMs that consider the context of the query.
• Disambiguation Techniques: Implement algorithms to distinguish between different meanings based on query context.

Computational Resources

Generating and processing multiple expanded queries can be resource-intensive.

Mitigation:

• Efficient Models: Use optimized LLMs and retrieval systems.
• Caching Mechanisms: Cache frequent queries and expansions to reduce computation.

Integration with Retrieval Systems

Ensuring that the expanded queries work effectively with the existing retrieval algorithms.

Mitigation:

• Scoring Adjustments: Modify retrieval scoring to account for expanded queries.
• Hybrid Approaches: Combine keyword-based and semantic retrieval methods.

Techniques for Effective Query Expansion

Term Weighting

Assigning weights to terms in the expanded queries to reflect their importance.

• TF-IDF (Term Frequency-Inverse Document Frequency): Measures how important a term is in a document relative to a corpus.
• BM25 Scoring: A ranking function used by search engines to estimate the relevance of documents.
• Custom Weights: Adjust weights based on the relevance of the expanded terms.

Re-Ranking Retrieved Documents

After retrieval, re-ranking the documents to prioritize relevance.

• Cross-Encoders: Use models that assess the relevance of query-document pairs.
• Re-Ranking Models (e.g., ColBERT, FlashRank): Specialized models that provide efficient and accurate re-ranking.

Example:

Using a Cross-Encoder after retrieval to score and re-rank documents based on their relevance to the original query.

Leveraging User Feedback

Incorporating user interactions to improve query expansion.

• Implicit Feedback: Analyze user behavior, such as clicks and time spent on documents.
• Explicit Feedback: Allow users to refine queries or select preferred results.

Connection with AI, AI Automation, and Chatbots

AI-Powered Query Expansion

Using AI and LLMs for query expansion leverages advanced language understanding to improve retrieval. This enables AI systems, including chatbots and virtual assistants, to provide more accurate and contextually appropriate responses.

Automation in Information Retrieval

Automating the query expansion process reduces the burden on users to craft precise queries. AI automation handles the complexity behind the scenes, enhancing the efficiency of information retrieval systems.

Enhancing Chatbot Interactions

Chatbots benefit from query expansion by better understanding user intents, especially in cases where users use colloquial language or incomplete phrases. This leads to more satisfying interactions and effective problem-solving.

Example:

A chatbot assisting with technical support can interpret a user’s vague query like “My app isn’t working” by expanding it to include “application crashes,” “software not responding,” and “app error messages,” leading to a faster resolution.

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Research on Query Expansion for RAG

1. Improving Retrieval for RAG based Question Answering Models on Financial Documents
   This paper examines the effectiveness of Large Language Models (LLMs) enhanced by Retrieval-Augmented Generation (RAG), particularly in financial document contexts. It identifies that inaccuracies in LLM outputs often arise from suboptimal text chunk retrieval rather than the LLMs themselves. The study proposes improvements in RAG processes, including sophisticated chunking techniques and query expansion, along with metadata annotations and re-ranking algorithms. These methodologies aim to refine text retrieval, thereby improving LLM performance in generating accurate responses. Read more
2. Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
   The paper introduces a modular approach to enhance RAG systems, focusing on the Query Rewriter module, which creates search-friendly queries to improve knowledge retrieval. It addresses issues of Information Plateaus and Ambiguity in queries by generating multiple queries. Additionally, the Knowledge Filter and Memory Knowledge Reservoir are proposed to manage irrelevant knowledge and optimize retrieval resources. These advancements aim to boost response quality and efficiency in RAG systems, validated through experiments across QA datasets. Access the code and more details.
3. MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
   This research highlights challenges in existing RAG systems when dealing with multi-hop queries, which require reasoning over multiple pieces of evidence. It introduces a novel dataset specifically designed to benchmark RAG systems on multi-hop queries, aiming to push the boundaries of current RAG capabilities. The paper discusses advancements necessary for RAG methods to effectively handle complex query structures and improve LLM adoption for practical applications.