📚 ReadyCite

AI-Driven Fact Checking for Scientific Citations

🎯 Project Overview

ReadyCite is an innovative machine learning system designed to address a critical challenge in scientific literature: quotation errors. By automatically detecting inaccurate citations, this project aims to enhance the integrity and reliability of academic publications.

✨ Key Features

• Automated Citation Checking: Identify citations that do not accurately support the claims made in scientific manuscripts
• Multi-Dataset Training: Leverages diverse datasets including SciFact, Citation-Integrity, and Fever for comprehensive learning
• Advanced NLP Techniques: Utilizes state-of-the-art natural language processing models like Longformer and BERT
• Scalable Architecture: Designed to process massive scientific literature datasets

🔍 Problem Statement

Quotation errors are unfortunately common in scientific literature, with studies suggesting 10-20% of citations may be inaccurate. These errors can:

• Undermine research credibility
• Propagate misinformation
• Waste researchers' time in tracing citation origins

🛠 Technical Approach

The project is structured in three progressive phases:

1. Initial Model Training

   • Use preprocessed datasets with abstracts and evidence sentences
   • Develop baseline citation accuracy classification

2. Evidence Retrieval Pipeline

   • Expand from abstracts to full-text PDF processing
   • Implement advanced sentence retrieval techniques (BM25, MonoT5 reranking)

3. Large-Scale Deployment

   • Cloud deployment
   • Processing extensive databases like bioRxiv

📊 Data Sources

• SciFact: 1,409 scientific claims verified against 5,183 abstracts
• Citation-Integrity: 3,063 citation instances from biomedical publications
• Fever: 185,445 claims from Wikipedia (pre-training)

🚀 Planned Deliverables

• Web application for citation accuracy verification
• Open-source machine learning models
• Detailed research documentation

🧰 Technologies

• Python
• PyTorch
• Transformers (BERT, Longformer)
• Natural Language Processing Libraries
• Machine Learning Frameworks

📋 Project Status

Current Phase: Model Training and Validation

• Reproducing Citation-Integrity model
• Experimenting with training epochs and datasets

🤝 Contributing

Interested in improving scientific literature integrity? Contributions are welcome! Please read our contribution guidelines.

📞 Contact

Jeffrey Dick - Project Lead