Unleashing the Power of RAG
Retrieval-Augmented Generation (RAG) is a cutting-edge technique that merges the best of both worlds: retrieving relevant information and generating human-like responses. Imagine asking a question and getting an answer that doesn't just rely on pre-existing data but also creates new content based on that data. This is what RAG does—combining the accuracy of search engines with the creativity of language models.
In a nutshell, RAG works like this:
- Understanding the Question: The system converts your question into a digital format it can understand.
- Fetching Information: It then searches through vast amounts of data to find the most relevant documents.
- Crafting a Response: Using these documents, the system generates a detailed and context-rich answer.
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| Traditional RAG System |
This approach is great, but we can make it even better with advanced techniques like Query Expansion and Retrieved Documents Reranking.
Broadening Horizons with Query Expansion
Query Expansion is like brainstorming extra questions to ensure you get the most relevant information. It enhances the original question by generating related queries that cover different angles. This helps the system retrieve a broader set of documents, increasing the chances of finding the best information. For example, if you ask about a clothing item, Query Expansion might add related questions about size, colour, material, and reviews. This technique uses a concept called Hypothetical Document Embeddings (HyDE), which generates hypothetical answers to broaden the search [1].
Example Prompt for Query Expansion
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| RAG with Query Expansion |
Challenges of Query Expansion
Despite its benefits, Query Expansion can introduce some challenges:
- Increased Noise: More questions can lead to retrieving irrelevant documents.
- Redundancy: Overlapping documents from multiple queries can waste resources.
- Context Overload: Too many documents can overwhelm the system, making it harder to generate a clear response.
These challenges highlight the need for refining the retrieved documents, which brings us to the next advanced technique.
Fine-Tuning with Retrieved Documents Reranking
Retrieved Documents Reranking is like prioritising the most important information from a list of results. This technique ensures that the most relevant documents are given precedence, addressing the issues brought by Query Expansion.
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| RAG with Query Expansion and Reranking |
Why Reranking is Crucial
- Limited Context Window: Language models can only process a limited amount of information at once. Reranking makes sure the most relevant documents are included within this limit.
- Improved Recall Performance: With too much information, models can miss crucial details. This is known as "Lost in the Middle," where important data buried in a large context is overlooked. Reranking helps to avoid this by placing key documents upfront.
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| When information is placed in the middle of a context window, an LLM's recall ability is diminished compared to if the information were never included [2]. |
Cross-Encoder Reranking
Cross-Encoder Reranking is a sophisticated method that evaluates the relevance of each document with respect to the query by considering both the query and the document simultaneously. Unlike traditional reranking methods, such as Bi-Encoders, that treat query and documents separately, cross-encoders use a more integrated approach, which typically results in more accurate ranking.
In this technique, the query and each retrieved document are encoded together, and a relevance score is computed for each pair. This process allows the system to capture nuanced relationships between the query and documents, leading to more precise reranking.
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| Cross-Encoder Reranker |
Wrapping Up
By integrating advanced techniques like Query Expansion and Retrieved Documents Reranking, including Cross-Encoder Reranking, RAG becomes even more powerful and efficient. These enhancements tackle the inherent limitations of language models, such as context constraints and recall performance, leading to more accurate and relevant responses.
Expanding queries ensures a comprehensive search, while reranking fine-tunes the results, making RAG systems smarter and more effective.
References
- L. Gao, X. Ma, J. Lin, and J. Callan, "Precise Zero-Shot Dense Retrieval without Relevance Labels", 2022.
- N. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, "Lost in the Middle: How Language Models Use Long Contexts", 2023.
