Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.03582 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909241489489920 |
|---|---|
| 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 |