This repository hosts the ongoing study detailed in the "VRPG for Fine Tunings Goals and Path Finding" paper. The study delves into the realm of reducing complex text to guides and optimizing goals within the context of large language models. It represents an exploratory journey into the intersection of cognitive theories and computational linguistics, striving to create a general framework for goal optimization.
- Focus: The study primarily focuses on transforming qualitative data into quantifiable goals, leveraging the power of large language models.
- VRPG Model: The core of the study revolves around the VRPG (Values, Resources, Problems, Goals) model, a novel approach to understanding and navigating the complexities inherent in setting and achieving various goals.
- Goal Optimization: The paper outlines methodologies for breaking down abstract goals into concrete steps and measurable outcomes.
- Matrix and Vector Approach: A key contribution is the development of a matrix and vector approach, adding a layer of sophistication to the analysis of how resources, values, and problems interact to influence goals.
- Paper PDF: Full text of the "VRPG for Fine Tunings Goals and Path Finding" paper.
- Supplementary Materials: Additional documents and resources that support the findings and methodologies presented in the study.
- Example Implementations: Sample codes or algorithms illustrating the application of the VRPG model and the matrix-vector approach.
- Data Sets: If applicable, data sets used in the study for analysis and model training.
- For Researchers: Dive into the paper for a comprehensive understanding of the VRPG model and its applications in goal optimization using large language models.
- For Developers: Explore the example implementations to understand how the theoretical concepts are translated into practical algorithms.
- For Enthusiasts: Leverage the supplementary materials to gain additional insights into the study and its broader implications in the field of AI and cognitive sciences.
We welcome contributions from the community. Whether it's suggesting improvements, extending the existing model, or providing new insights, your input is valuable. Please refer to the contributing guidelines for more details on how to participate.
If you use the findings or methodologies from this study in your work, please cite the paper as follows:
@article{vrpg_study_2023,
title={VRPG for Fine Tunings Goals and Path Finding},
author={Saransh Sharma},
year={2023},
url={}
}
The contents of this repository are licensed under [Appropriate License], allowing for both academic and commercial use, with appropriate credit.
For queries or collaborations, please reach out to the author or the research team at [email protected].