Use this Technique to Reduce LLMs Cost by Over 50%

Large Language Models (LLMs) have revolutionized the way we interact with and utilize artificial intelligence. From generating text to answering complex questions, their versatility is unmatched. However, this power comes at a significant cost — API usage, measured in tokens, can quickly escalate, making these solutions prohibitively expensive for many individuals and organizations.

Reducing token usage while maintaining output quality is a crucial challenge for making LLMs more accessible and affordable. This is where prompt compression comes into play. By strategically shortening input prompts, we can drastically cut costs without compromising the quality or fidelity of the model’s responses.

In this article, we’ll explore LLMLingua-2, a novel method for efficient and faithful task-agnostic prompt compression. Developed by researchers at Microsoft, LLMLingua-2 leverages data distillation to learn compression targets, offering a robust approach to minimize token usage while preserving performance across various tasks.

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