A Double Clustering Strategy to Sharpen Large Language Models for Data-to-Text Tasks
A new method for selecting in-context examples significantly improves the efficiency and performance of large language models (LLMs) in data-to-text generation. The approach, called Double Clustering-based In-Context Example Selection, operates on the hypothesis that optimal examples must be both highly similar to the input data and diverse from each other. It employs two distinct clustering stages to maximize these properties, coupled with a batched generation technique to enhance token usage efficiency. This research addresses a critical bottleneck in prompt engineering for generative AI, demonstrating that strategic example selection can boost accuracy while reducing computational cost and time, a key advancement for practical applications of transformers and foundation models in natural language processing.
Study Significance: For professionals leveraging large language models, this work provides a concrete, optimized framework for prompt engineering that directly impacts model efficiency and output quality. It moves beyond trial-and-error example selection, offering a principled, data-driven method that can reduce operational costs and improve the reliability of AI-generated content. This development is particularly relevant for applications requiring consistent, high-quality text generation from structured data, enabling more scalable and effective use of generative AI in automated reporting and content creation systems.
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