Solving Information Retrieval Challenges with APEER
Automating Prompt Engineering for Enhanced LLM Performance
A significant challenge in Information Retrieval (IR) using Large Language Models (LLMs) is the heavy reliance on human-crafted prompts for zero-shot relevance ranking. This dependence requires extensive human effort and expertise, making the process time-consuming and subjective.
Current methods for addressing this challenge involve manual prompt engineering, which is time-consuming and subjective. APEER automates prompt engineering through iterative feedback and preference optimization, reducing human effort and significantly improving LLM performance across various datasets and models.
A team of researchers from Rutgers University and the University of Connecticut proposes APEER (Automatic Prompt Engineering Enhances LLM Reranking), which minimizes human involvement by generating refined prompts based on performance feedback and aligning them with preferred prompt examples.
APEER operates by initially generating prompts and refining them through two main optimization steps: feedback optimization and preference optimization. The method demonstrates significant improvements in LLM performance for relevance ranking tasks, exhibiting better transferability across diverse tasks and LLM architectures.
Unlocking the Potential of AI with APEER
Enhancing Your Business with AI Solutions
If you want to evolve your company with AI, stay competitive, and use APEER for your advantage. APEER represents a substantial advancement in the field, providing a scalable and effective solution for optimizing LLM prompts in complex relevance ranking scenarios.
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