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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.15148950 |
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Table of Contents:
- <p>This document proposes a lightweight architecture for filtering rare but semantically high-value conversation windows in LLM training, leveraging rare-hit triggers as scoring conditions. It outlines a prototype-phase pipeline and tiered annotation strategy aimed at extracting more meaningful data from long-form dialogue.</p> <p> The concept originated from user-side observation and reverse-engineering of language model response behavior. Language structuring and formatting were assisted by ChatGPT-4o.</p> <p>This document is released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license.<br>It may be freely shared, cited, and adapted—including for commercial use—<strong>provided that proper credit is given to the original author, Rhy Wolevon</strong>.</p>