Chunking Optimization for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) systems improve the response quality and reduce hallucinations in Large Language Models (LLMs) by retrieving relevant data from external sources and providing additional context. Most RAG models employ the Dual-Encoder Architecture (DEA) framework, where reference documents are segmented, encoded, and stored as embeddings in a vector database like FAISS or Neo4j. DEA offers a structured way to integrate diverse knowledge sources such as textbooks, knowledge graphs, and encyclopedias, thereby reducing hallucinations in LLMs. However, despite these advantages, the effectiveness of an RAG system heavily depends on how reference documents are chunked and indexed within the database. Optimizing the chunking process remains a core challenge in enhancing retrieval quality and response generation.

The Challenge of Chunking Optimization

One of the biggest hurdles in making RAG systems better has to do with how we break up important reference documents. Different sources of knowledge have their own unique ways of organizing information and how much they pack into a given space. Take textbooks, for example. They have long sections of text that all connect to each other. On the flip side, knowledge graphs are made up of short terms and how different things relate to one another. Because these sources pack in information so, we need to think about breaking them up into chunks of different sizes.

Picking the best chunk size by hand is a hassle and doesn't work well. Even when you choose a chunk size, it doesn't perform the same across different data sources. Users ask all sorts of questions that need different levels of detail. Specific questions work better with smaller chunks, while broad questions need bigger chunks. The need to handle these changes on the fly shows we need a way to adjust chunk sizes based on what the question asks and how the document is set up. Because of this, people are now focusing on adaptive chunking methods to make RAG systems work better.

Chunking Strategies

• Fixed-Size Chunking: This method offers the simplest way to break up documents. It cuts documents into pieces of the same size, which helps keep indexing and retrieval consistent. While it's easy to do, it often has trouble keeping the context intact. This happens because the random cut-offs can split important information into different chunks.
• Semantic Chunking: This approach uses language analysis tools to split text based on how sentences are built, where paragraphs end, or when topics change. This helps keep the meaning of each chunk intact, which makes retrieval work better. But to analyze text structure semantic chunking needs complex language models, which makes the process more demanding on computers.
• Sliding Window Chunking: This technique makes chunks that overlap each other. This helps make sure no key information gets lost at the edges of chunks. It works well when searches need to find everything related to a topic. By using overlapping chunks, it cuts down the risk of losing context which can happen with strict splitting methods.
• Adaptive Chunking: It utilizes algorithms that actively modify chunk sizes based on the document's nature and the query's needs. By analyzing document layouts and user questions, adaptive chunking dynamically decides the best way to divide the content.This method enhances retrieval efficiency by optimizing chunk sizes for different query types.

• Hierarchical Chunking: This breaks documents into multi-level chunks, allowing retrieval systems to consider both fine-grained and coarse-grained information. This hierarchical approach improves retrieval accuracy by offering multiple levels of granularity, enabling better response formulation.

   

Advanced Chunking Strategies

The Mix-of-Granularity (MoG) Approach

To further optimize chunking, the Mix-of-Granularity (MoG) method is introduced. MoG is inspired by the Mix-of-Experts architecture, a machine-learning approach that combines outputs from specialized models to optimize predictions. Similarly, MoG employs a trained router to select and combine reference snippets of varying granularity levels dynamically. The MoG Approach ensures that only the most relevant information is passed to the LLM to improve response accuracy.

The router in MoG is trained using supervised learning, enabling it to determine the ideal chunk size for each query. MoG dynamically selects relevant snippets, striking a balance between information coverage and relevance, which results in better-grounded responses from the LLM. Unlike fixed or manually tuned chunking methods, MoG adapts to diverse query requirements, significantly enhancing retrieval precision and reducing redundant information retrieval.

Extending MoG to Mix-of-Granularity-Graph (MoGG)

While MoG significantly improves chunking optimization, it struggles with complex queries that require information from multiple documents or different knowledge bases. In such cases, adjusting chunk sizes alone is insufficient, as the relevant data may be spread too far apart to be captured within a single chunk.

To tackle this issue, experts have expanded MoG into the Mix-of-Granularity-Graph (MoGG). MoGG turns reference documents into a graph structure before processing and connecting related snippets, even when they're not next to each other in the original document. This allows for better retrieval of scattered information and boosts the model's capability to handle queries across multiple documents.

By turning knowledge bases into graphs, MoGG keeps relevant data easy to access, no matter where it was. This upgrade improves the retrieval process for complex queries that involve multiple sources. Take, for instance, a legal or technical query that needs information from several regulations or research papers. MoGG finds relevant parts from different documents and gives a combined answer.

Overcoming Training Challenges with Soft Labels

Many RAG systems depend on a top-k selection method to fetch relevant document snippets from a database. This approach works well to limit search scope, but it stops gradient backpropagation, which makes training hard to do. The fact that retrieval models can't be fine-tuned well reduces how adaptable RAG systems can be. To fix this, MoGG brings in a loss function that uses soft labels. These soft labels serve as rough training signals and come from offline algorithms like TF-IDF or models such as RoBERTa. Using soft labels in training gets rid of the need for top-k selection letting gradient backpropagation happen. This change speeds up training without hurting retrieval accuracy. Soft labels help the model learn retrieval patterns well, making both recall and relevance better in document retrieval.

Conclusion

Chunking optimization plays a key part in making the retrieval phase of RAG systems better. Good chunking strategies make sure LLMs get the most relevant info, leading to more accurate and context-appropriate responses.

The introduction of advanced techniques such as MoG, MoGG, and Windowed Summarization further enhances chunking efficiency. MoG dynamically selects chunk sizes based on query requirements, MoGG structures reference documents into graphs to improve retrieval for dispersed information, and Windowed Summarization maintains critical insights across overlapping sections.

Also, using soft label-based training helps get around the limits of top-k selection, which makes the model work better. By using these methods together, RAG systems can boost how well they retrieve information. This leads to more accurate and relevant responses.