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.

Discover how the Mix-of-Granularity (MoG) and Mix-of-Granularity-Graph (MoGG) approaches optimize the traditional chunking strategies. Read the full article here: Chunking Optimization for Retrieval-Augmented Generation