README.md

metadata

title: Agri-Critique Research Assistant
emoji: 🌾
colorFrom: green
colorTo: yellow
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit

Agri-Critique: Self-Correcting Agricultural QA System

Overview

Agri-Critique is an evidence-based question-answering system for agricultural research, powered by a fine-tuned Llama 3.1 8B model trained on 2,859 QA pairs with self-correction capabilities.

Key Features

• 🎯 Evidence-Grounded Answers: All responses cite scientific papers
• 🔍 Hallucination Detection: Trained to identify and correct errors
• 🔎 Critique Mode: Validate any answer for accuracy and errors
• 💬 Conversational: Maintains context across multiple questions
• 📚 Citation-Aware: Provides paper references for verification

Two Interaction Modes

1. 💬 Q&A Mode: Ask questions and get evidence-based answers
2. 🔍 Critique Mode: Submit a question + proposed answer to validate for:
   • Hallucinations (fake findings)
   • Unsupported claims
   • Temporal/causal errors
   • Missing citations

Model Details

• Base Model: Llama 3.1 8B Instruct
• Fine-tuning: LoRA (rank=32, alpha=64)
• Training Data:
  • 1,814 standard QA pairs (63.4%)
  • 1,045 adversarial critique pairs (36.6%)
• Training Objective: Mixed curriculum (answer + critique)

Dataset Composition

The model was trained on Agri-Critique, a novel dataset combining:

1. Standard QA: Evidence-based agricultural questions
2. Adversarial QA: Intentionally flawed answers with critiques
   • Hallucinations (fake findings)
   • Temporal mismatches
   • Causal reversals
   • Unsupported extrapolations

Research Contribution

This system demonstrates:

• Self-Correction: Models can learn to detect their own errors
• Citation Integrity: Reduces "fake citation" problem in RAG systems
• Domain Adaptation: Specialized for agricultural/scientific QA

Usage

1. Type your agricultural research question
2. View the evidence-based answer with citations
3. Expand "View Evidence Sources" to see paper excerpts
4. Ask follow-up questions for multi-turn conversation

Example Questions

• "What is the effect of nitrogen fertilizer on rice yield?"
• "How does drought stress affect wheat production?"
• "What are the benefits of crop rotation?"
• "Explain the impact of climate change on agriculture."

Limitations

• Domain-Specific: Optimized for agricultural/scientific questions
• Evidence-Dependent: Answers limited to indexed papers
• Not Hallucination-Free: While reduced, some errors may occur
• English Only: Currently supports English language only

Citation

If you use this system in your research, please cite:

@article{agri-critique-2025,
  title={Agri-Critique: Training Self-Correcting Language Models for Agricultural Question Answering},
  author={Your Name},
  journal={arXiv preprint},
  year={2025}
}

Links

• 📄 Paper (Coming soon)
• 💻 GitHub
• 🤗 Model
• 📊 Dataset

License

MIT License - See LICENSE file for details

Acknowledgments

Built with:

• Llama 3.1 by Meta
• Hugging Face Transformers
• FAISS for vector search
• Streamlit for UI