Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Content
<|reference_start|>Integrating Randomness in Large Language Models: A Linear Congruential Generator Approach for Generating Clinically Relevant Content: 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.<|reference_end|>
@article{bouras2024integrating,
    title={Integrating Randomness in Large Language Models: A Linear Congruential
  Generator Approach for Generating Clinically Relevant Content},
    author={Andrew Bouras},
    journal={arXiv preprint arXiv:2407.03582},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03582},
    primaryClass={cs.CL cs.AI}
}