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An Ethical Framework for Training Large Language Models (LLMs) to Navigate the Landscape of Proprietary Research Data

Authors: Mareya, Liberty Artwell; Moto, Michael R.R;

An Ethical Framework for Training Large Language Models (LLMs) to Navigate the Landscape of Proprietary Research Data

Abstract

The ever-growing volume of proprietary research data presents both a challenge and an opportunity for scientific advancement. This data holds immense potential for groundbreaking discoveries and transformative technologies. However, extracting its full value remains a complex task. Traditional research methods often struggle to identify the intricate connections and hidden patterns buried within these vast datasets. Large Language Models (LLMs) offer a powerful new tool to navigate this complex data landscape. However, leveraging LLMs for scientific research necessitates a robust ethical framework to address critical concerns surrounding data privacy, security, and potential biases within the models themselves. This paper proposes a comprehensive approach for Xyberius Enterprises to harness the power of LLMs ethically and responsibly, unlocking the full potential of theirproprietary research data for scientific progress.

Keywords

Ethics, New frontiers, LLMs, Proprietary, Research Data

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average