Large language models (LLMs) have become increasingly popular in recent years, due to their ability to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, the cost of using LLM APIs can be prohibitive for some users.
A recent study by Stanford University researchers, titled "FrugalGPT," presents a number of techniques for reducing the cost of using LLM APIs by up to 98%. These techniques include:
- Prompt adaptation: The length of the prompt is a major factor in the cost of using an LLM API. By shortening the prompt, users can significantly reduce the cost of their API calls.
- LLM approximation: In some cases, it is possible to approximate the performance of a costly LLM using a less expensive model. For example, the researchers found that it was possible to achieve similar results to GPT-4 using a model that was only 1% of the size.
- Query concatenation: By combining multiple prompts into a single query, users can reduce the number of API calls they need to make. This is particularly effective for tasks that require few-shot prompting.
- Prompt selection: The researchers found that it was possible to reduce the cost of few-shot prompting by removing unnecessary examples from the prompt.
The techniques described in the FrugalGPT study can provide significant savings for users of LLM APIs. By following these techniques, users can reduce their costs while still maintaining or even improving the performance of their applications.
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