ADO: Automatic Data Optimization for Inputs in LLM Prompts
This addresses the challenge of improving LLM efficiency for users in natural language processing, but it is incremental as it builds on existing prompt engineering methods.
This paper tackles the problem of enhancing Large Language Model performance by optimizing input data within prompts, introducing a two-pronged strategy of content engineering and structural reformulation that significantly improves performance in various tasks.
This study explores a novel approach to enhance the performance of Large Language Models (LLMs) through the optimization of input data within prompts. While previous research has primarily focused on refining instruction components and augmenting input data with in-context examples, our work investigates the potential benefits of optimizing the input data itself. We introduce a two-pronged strategy for input data optimization: content engineering and structural reformulation. Content engineering involves imputing missing values, removing irrelevant attributes, and enriching profiles by generating additional information inferred from existing attributes. Subsequent to content engineering, structural reformulation is applied to optimize the presentation of the modified content to LLMs, given their sensitivity to input format. Our findings suggest that these optimizations can significantly improve the performance of LLMs in various tasks, offering a promising avenue for future research in prompt engineering. The source code is available at https://anonymous.4open.science/r/ADO-6BC5/