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Auteur principal: Bouras, Andrew
Format: Preprint
Publié: 2024
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Accès en ligne:https://arxiv.org/abs/2407.03582
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author Bouras, Andrew
author_facet Bouras, Andrew
contents Generating diverse, high-quality outputs from language models is crucial for applications in education and content creation. Achieving true randomness and avoiding repetition remains a significant challenge. This study uses the Linear Congruential Generator method for systematic fact selection, combined with AI-powered content generation. We ensured unique combinations of gastrointestinal physiology and pathology facts across multiple rounds, integrating these facts into prompts for GPT-4o to create clinically relevant, vignette-style outputs. Over 14 rounds, 98 unique outputs were generated, demonstrating LCG's effectiveness in producing diverse and high-quality content. This method addresses key issues of randomness and repetition, enhancing the quality and efficiency of language model-generated content for various applications.
format Preprint
id arxiv_https___arxiv_org_abs_2407_03582
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Content
Bouras, Andrew
Computation and Language
Artificial Intelligence
Generating diverse, high-quality outputs from language models is crucial for applications in education and content creation. Achieving true randomness and avoiding repetition remains a significant challenge. This study uses the Linear Congruential Generator method for systematic fact selection, combined with AI-powered content generation. We ensured unique combinations of gastrointestinal physiology and pathology facts across multiple rounds, integrating these facts into prompts for GPT-4o to create clinically relevant, vignette-style outputs. Over 14 rounds, 98 unique outputs were generated, demonstrating LCG's effectiveness in producing diverse and high-quality content. This method addresses key issues of randomness and repetition, enhancing the quality and efficiency of language model-generated content for various applications.
title Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Content
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2407.03582