How to Cut Your LLM Classification Costs by 90%
Summary
This session details the Model Cascade architecture, a technique to drastically reduce LLM classification costs (by over 90%) while maintaining high accuracy (e.g., 95%+). Instead of running an expensive 'Oracle' model on every record, the cascade uses a cheap proxy model for initial predictions and only escalates to the Oracle when the proxy's confidence score is low. The optimal decision threshold ($\tau$) is found offline by simulating performance across samples.
Key takeaways
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Model Cascade Architecture
8:31
The pipeline first sends every record (e.g., support ticket) to a cheap LLM API (the proxy/nano model). It generates a prediction $P(x)$ and a confidence score $C(x)$. A decision point then determines if the score $C(x)$ is high or low, routing only low-confidence records to the expensive 'Oracle' model.
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Cost Optimization Principle
17:31
The goal is to find a threshold $\tau$ that minimizes cost while maintaining a target accuracy (e.g., 95%) relative to the Oracle model. Lowering $\tau$ increases cost savings but risks dropping below the required accuracy.
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Threshold Determination
20:48
The optimal threshold $\tau$ is found offline by simulating the cascade using a labeled sample of tickets, calculating the resulting accuracy and cost for various potential thresholds $C(x_i)$.
Technical details
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Model Cascade Algorithm Steps
1248s
1. Sample Selection: Run the proxy model on a representative sample of records (e.g., 200-500 tickets) to obtain $P(x_i)$ and $C(x_i)$. 2. Labeling: Obtain ground truth labels $O(x_i)$ from the Oracle for the sample. 3. Threshold Simulation: Simulate the cascade using various thresholds $\tau$ on the sample data, recording resulting accuracy and cost pairs. 4. Optimization: Select the lowest threshold $\tau$ that meets or exceeds the target accuracy while minimizing total cost.
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Cascade Inference
For all tickets $X$: If $C(x) \ge \tau$, accept $P(x)$. Otherwise, run $O(x)$ (the Oracle).
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Statistical Guarantees
The procedure is a best estimate based on samples. For statistical guarantees, concentration inequalities can be used instead of naive sample accuracy calculation.
Mentioned resources
Channel & topics
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