## Introduction

Over time, content platforms have shifted from open, collaborative environments to highly structured systems governed by detailed style and compliance guides. This evolution, while enhancing the quality and consistency of content, has also introduced complexity to the creation and review processes. To address these challenges, we introduce **Omnipedia**, a language model pipeline designed to transform any style guide or manual into a set of context-sensitive requirements for automated document evaluation, providing structured and precise feedback on compliance.

Omnipedia leverages large language models to assess each sentence within an article, providing scores and actionable feedback to enhance article quality. This paper outlines the broader context, solution architecture, specific application, and outcomes of the Omnipedia framework.

## Broader Context of the Research

### Traditional Systems Engineering Practices

In complex systems engineering, explicit structures such as finite element analysis are fundamental for ensuring system stability and providing a robust framework for analysis. These structures enable precise modeling, simulation, and management of system components, facilitating scalability and reliability in large-scale projects.

For instance, NASA's openMBEE team exemplifies the application of computational systems engineering to manage and maintain complex aerospace projects. They leverage formal specifications to ensure all system components function cohesively, demonstrating the power of structured approaches in high-stakes engineering environments.

### Natural Language Processing (NLP) and Large Language Models (LLMs)

In contrast to traditional engineering systems, NLP and LLMs rely on implicit structures, deriving meaning from context rather than predefined frameworks. While this approach allows for flexibility and adaptability in language understanding, it presents challenges in maintaining consistency and reliability, particularly in tasks requiring strict adherence to specific guidelines, such as Wikipedia article writing and editing.

### Intersection of Systems Engineering and NLP

The integration of systems engineering principles into NLP applications presents an opportunity to introduce explicit structures within language models. By formalizing guidelines and expectations, it's possible to enhance the stability and consistency of NLP-driven tasks, ensuring more reliable and scalable solutions for complex language processing challenges.

### Relevance to Wikipedia

Wikipedia's transition from a platform for draft content to a repository of polished articles necessitated the development of comprehensive style guides. As the volume of articles grew, so did the need for scalable review processes to maintain quality. Omnipedia addresses this need by automating the review process, reducing the manual workload on human reviewers, and ensuring consistent adherence to style guidelines across a vast and diverse corpus of content.

## Solution Architecture

### Overview

Omnipedia is designed to automate the evaluation of Wikipedia articles by transforming the Manual of Style into a set of context-sensitive, machine-executable requirements. The framework consists of a three-stage pipeline:

1. **Requirements Generation:** Extracting and formalizing style guidelines into specific, actionable requirements.
2. **Automated Evaluation:** Parsing articles into structured formats and assessing each section and sentence against the generated requirements using LLMs.
3. **Result Rendering:** Visualizing evaluation outcomes through annotated text and heat maps to provide clear feedback on compliance levels.

### Detailed Architecture

#### Core Components

- **StyleGuideProcessor:** Transforms `styleguide.txt` into a structured `requirements.json`, extracting sections and assigning relevant style rules.
- **ArticleParser:** Parses `article.md` into a hierarchical `ArticleNode` tree, handling different section types and structuring content for evaluation.
- **ArticleEvaluator:** Evaluates each `ArticleNode` against `requirements.json`, utilizing the LanguageModel API to generate compliance scores and feedback.
- **LanguageModel:** Interface for prompt-based evaluations, generating both qualitative and quantitative assessments.

#### Workflow Steps

1. **Initialization:** Load style guides and initialize system components.
2. **Style Guide Processing:** Parse the Manual of Style and extract formalized requirements.
3. **Article Parsing:** Convert articles into structured `ArticleNode` trees.
4. **Article Evaluation:** Assess each section and sentence for compliance, generating scores and feedback.
5. **Output Generation:** Present evaluation results through interactive interfaces and visual tools.

## The Omnipedia Application

### Purpose and Objectives

Omnipedia aims to automate the review process of Wikipedia articles by ensuring adherence to established style guidelines. The primary objectives include:

- **Scalability:** Efficiently handle the growing volume of Wikipedia articles.
- **Consistency:** Maintain uniform quality across all articles by adhering to style guidelines.
- **Efficiency:** Reduce manual workload on human reviewers through automation.
- **Actionable Feedback:** Provide clear and constructive feedback to authors for improving article quality.

### Key Features

- **Structured Evaluation:** Links specific style requirements to corresponding sections and sentences within articles.
- **Context-Sensitive Feedback:** Offers sentence-level and section-level evaluations with detailed suggestions for improvements.
- **Human-Machine Collaboration:** Combines automated assessments with human oversight to ensure reliability and accuracy.

### Advanced Capabilities

- **Multi-Agent, Multi-Prompt System:** Utilizes multiple LLM agents to handle parallel evaluations, enhancing efficiency.
- **Structured Retrieval-Augmented Generation (RAG):** Maintains high-quality outputs by locking in effective evaluations and avoiding redundant processing.

### User Interface

- **Annotated Viewing Interface:** Displays articles with color-coded highlights indicating compliance levels, allowing users to view detailed feedback and suggestions.