Starving yourself is unproductive, but what happens when you starve your LLMs…of context?

Author(s): Surya Maddula Originally published on Towards AI. Starving your LLMs might be the key to contextual prompt reduction. LLMs have remarkable capabilities for nlp tasks, but when deploying them, there’s always been a few challenges because of two main reasons: computational costs and memory constraints. And this is especially true when processing lengthy prompts. Image explaining prompt length challenges.The article discusses various strategies and techniques for reducing prompt sizes when working with large language models (LLMs) without sacrificing essential context or information. It introduces hard and soft prompt compression methods, highlights their benefits, and explains their applications across different scenarios, including retrieval-augmented generation, mathematical reasoning, and coding tasks. The piece concludes by emphasizing the necessity of understanding one’s specific requirements for selecting appropriate compression methods while acknowledging the trade-offs involved in achieving efficiency. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI