# Transforming the Output of Generative Pre-trained Transformer: The Influence of the PGI Framework on Attention Dynamics

arXiv: https://arxiv.org/abs/2308.13317 · HTML: https://mlsanity.com/paper/2308.13317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 25 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Analyzing the Structure of Attention in a Transformer Language Model](https://mlsanity.com/paper/1906.04284/) (2019)
- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://mlsanity.com/paper/2303.11032/) (2023)
- [Can GPT-4 Perform Neural Architecture Search?](https://mlsanity.com/paper/2304.10970/) (2023)
- [Patent Claim Generation by Fine-Tuning OpenAI GPT-2](https://mlsanity.com/paper/1907.02052/) (2019)
- [Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions](https://mlsanity.com/paper/2308.02312/) (2023)
- [When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism](https://mlsanity.com/paper/2201.10801/) (2022)
- [Automatic Prediction of Rejected Edits in Stack Overflow](https://mlsanity.com/paper/2210.03281/) (2022)

## Built on by (most cited first, 1 of 1)

- [Hallucinations or Attention Misdirection? The Path to Strategic Value Extraction in Business Using Large Language Models](https://mlsanity.com/paper/2402.14002/) (2024)

## Abstract (arXiv)

This paper presents a novel approach named Persona-Grouping-Intelligence (PGI), which has been crafted to tackle the challenges posed by GPT models when applied to real-world business issues. PGI leverages the inherent capabilities of the GPT model to comprehend intricate language structures and generate responses that are contextually relevant. The experiment occurred in a business scenario where human intelligence was being underutilized due to less optimized business processes. The primary objective of this approach is to leverage GPT models to reduce the workload on humans in tasks that are extensive, monotonous, and repetitive. Instead, the focus is redirected toward decision-making activities. Remarkably, the experiment yielded an accuracy rate of 93.81% in validating 4,000 responses generated by the model, underscoring the effectiveness of the PGI strategies. Effectively addressing the issue of underutilized human intelligence, this paradigm shift aligns business environments with dynamic machine intelligence, enabling them to navigate the intricacies of real-world challenges. This approach facilitates the practical utilization of these models to tackle actual problems. The methodology offers an opportunity to reshape the fundamental structure of business processes by seamlessly integrating human decision-making with adaptable machine intelligence. Consequently, this optimization enhances operational efficiency and elevates strategic decision-making across diverse business contexts.