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Classification Tree for LLMs
In document intelligence, classification is often the crucial first step. It sets the stage for subsequent processes like data extraction and analysis. However, just because it’s the first step doesn’t mean it’s easy. Challenges often arise when ensuring the correct classification, especially when dealing with a large number of similar document types. Consider the following example:
In these cases, two primary problems emerge:
- Larger Contexts: The more classifications you have, the larger the context required for the language model, which can exceed token limits and increase token count.
- Reduced Effectiveness: Documents with similar structures (e.g., an invoice and a credit note) can confuse the model, leading to misclassifications.
In previous articles, I covered advanced methods like the Mixture of Models (MoM) to tackle document classification. While effective, these approaches can become suboptimal as the number of classifications grows.
Solving the Problem
One effective strategy is to organize classifications into a hierarchical tree structure. This approach allows us to break down…
