An interactive exploration of a multi-layered, principled design for text coherence in the age of Large Language Models.
Effective text chunking is a balancing act. The goal is to create chunks small enough for precision but large enough to retain meaning. Most methods fail, leading to two critical problems: Context Fragmentation and Semantic Dilution. This section demonstrates these failures.
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Click the buttons to see how naive chunking strategies can break the text's semantic integrity.
The evolution of chunking can be seen as a progression through several paradigms. Each attempts to find the ideal semantically coherent unit, but with significant trade-offs. Select a paradigm below to compare its approach and effectiveness.
The LSC algorithm moves beyond simple splitting and topical similarity. It models a document as a multi-layered graph of meaning, integrating deep linguistic analysis to create truly coherent chunks. Explore its four-layer pipeline by clicking on each stage below.
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Select a layer on the left to see its details.
Standard metrics fail to measure what truly matters: the internal coherence of a chunk. We propose a new suite of linguistically-motivated metrics that assess the actual quality and self-containment of the generated chunks.
The field is evolving rapidly. While LSC offers a robust solution for today's challenges, new research is exploring paradigms that could reduce or even eliminate the need for pre-chunking, focusing instead on model architecture and data curation.
Approaches like Chunking-Free In-Context Retrieval (CFIC) aim to bypass the "split-then-embed" pipeline entirely, instead retrieving evidence directly from the hidden states of an entire document. This avoids any risk of context fragmentation.
Frameworks like ProLong focus on improving the LLM's intrinsic abilities by training it on a curated diet of documents rich in genuine long-range dependencies. A better model is less sensitive to imperfect chunking.