AI BriefingBuilding Custom Models Using Synthetic Data
Key point
This presents a method for building low-cost, high-efficiency custom models that achieve GPT-4-level performance by leveraging synthetic data generated by LLMs.
Details
When domain-specific datasets (e.g., finance) are scarce, a highly effective strategy is to use a general-purpose LLM to generate Synthetic Data and use it to train a smaller model.
In a case study on financial news sentiment analysis, training the open-source model RoBERTa on synthetic data achieved performance on par with GPT-4 while recording overwhelming efficiency gains.
Performance and Efficiency Comparison vs. GPT-4:
- Cost: $3,061 $\rightarrow$ $2.7
- Carbon Emissions: approx. 735~1100kg $\rightarrow$ 0.12kg
- Latency: several seconds $\rightarrow$ 0.13 seconds
- Accuracy/F1: identical at 94%
The process involves annotating data via the LLM API, then fine-tuning a specialized model based on that data using tools such as AutoTrain. This allows developers to gain both the convenience of LLMs and the control and efficiency of a custom model at the same time.
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