Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index

• URL: http://arxiv.org/abs/2412.01690v1
• Date: Mon, 02 Dec 2024 16:34:18 GMT
• Title: Can We Afford The Perfect Prompt? Balancing Cost and Accuracy with the Economical Prompting Index
• Authors: Tyler McDonald, Anthony Colosimo, Yifeng Li, Ali Emami,
• Abstract summary: We present the Economical Prompting Index (EPI), a novel metric that combines accuracy scores with token consumption.<n>Our study examines 6 advanced prompting techniques, including Chain-of-Thought, Self-Consistency, and Tree of Thoughts.
• Score: 5.714609806192087
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As prompt engineering research rapidly evolves, evaluations beyond accuracy are crucial for developing cost-effective techniques. We present the Economical Prompting Index (EPI), a novel metric that combines accuracy scores with token consumption, adjusted by a user-specified cost concern level to reflect different resource constraints. Our study examines 6 advanced prompting techniques, including Chain-of-Thought, Self-Consistency, and Tree of Thoughts, across 10 widely-used language models and 4 diverse datasets. We demonstrate that approaches such as Self-Consistency often provide statistically insignificant gains while becoming cost-prohibitive. For example, on high-performing models like Claude 3.5 Sonnet, the EPI of simpler techniques like Chain-of-Thought (0.72) surpasses more complex methods like Self-Consistency (0.64) at slight cost concern levels. Our findings suggest a reevaluation of complex prompting strategies in resource-constrained scenarios, potentially reshaping future research priorities and improving cost-effectiveness for end-users.

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