Эффективный алгоритм искусственного интеллекта для определения пользовательских предпочтений с помощью LLMs

 CIPHER: An Effective Retrieval-based AI Algorithm that Infers User Preference by Querying the LLMs


Language Models and User Preferences

Language models built on Large Language models (LLMs) have limitations in adapting to user preferences and specific tasks. Researchers have explored interactive learning of language agents depending on user edits to the agent’s output. However, handling user preferences can be complicated and subtle, creating a learning problem.

Introducing CIPHER

A team of researchers introduced CIPHER, a powerful algorithm designed to address the complexities of user preferences. CIPHER excels in achieving the lowest edit distance cost and retrieving inferred preferences from historical contexts to generate context-specific responses. It outperforms other baseline methods in cost reduction and preference accuracy.

Practical Applications

CIPHER achieves significant cost reduction in tasks such as summarization and email writing. It is cost-effective, highly efficient, and easier to understand than other baseline methods, making it a valuable tool for AI applications.

Implementing AI Solutions

If you want your company to thrive with the help of artificial intelligence (AI) and remain a leader, consider implementing CIPHER for effective retrieval-based AI solutions. Analyze how AI can transform your work, identify areas for automation, and determine the key performance indicators (KPIs) you want to improve with AI. Start with small AI projects, analyze the results and KPIs, and gradually expand automation based on the data and experience.

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