https://github.com/soumya-thoutam/data-driven-reasoning-analysis

Data analysis and NLP modeling of the ARC dataset to improve reasoning-based question answering.
https://github.com/soumya-thoutam/data-driven-reasoning-analysis

data-analysis data-modeling data-visualization nlp python

Last synced: 11 months ago
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Data analysis and NLP modeling of the ARC dataset to improve reasoning-based question answering.

• Host: GitHub
• URL: https://github.com/soumya-thoutam/data-driven-reasoning-analysis
• Owner: soumya-thoutam
• Created: 2025-01-12T02:37:37.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-12T08:24:14.000Z (about 1 year ago)
• Last Synced: 2025-01-23T04:32:55.946Z (about 1 year ago)
• Topics: data-analysis, data-modeling, data-visualization, nlp, python
• Language: Jupyter Notebook
• Homepage: https://soumya-thoutam.medium.com/data-driven-reasoning-analysis-680879d37064
• Size: 144 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Data-Driven Reasoning Analysis

### 🚀 Project Overview

The A_NLP_ARC project demonstrates how data-driven insights and advanced NLP techniques can solve challenging reasoning-based multiple-choice questions using the AI2 Reasoning Challenge (ARC) dataset.

This project highlights your ability to work with both structured and unstructured data, showcasing data manipulation, model fine-tuning, and analysis to generate actionable insights and tackle real-world problems in natural language understanding.

### 💡 Project Highlights

Working with both structured and unstructured data is a critical skill. This project emphasizes:

* **Data Manipulation Skills**: Preprocessing large-scale, unstructured datasets like the ARC dataset.

* **Modeling Techniques**: Fine-tuning pre-trained models and analyzing their performance.

* **Analytical Thinking**: Using logical and statistical evaluations to solve complex reasoning problems.

### 📚 Dataset

The AI2 Reasoning Challenge (ARC) dataset is designed to test natural language understanding and reasoning abilities in AI systems. It includes multiple-choice questions that require models to comprehend and infer meaning from the provided text.

**Dataset Features:**

1. ARC Easy: Questions solvable with retrieval-based methods.

2. ARC Challenge: Questions requiring advanced reasoning and inference.

   

**Access the Dataset:** [ARC Dataset](https://huggingface.co/datasets/allenai/ai2_arc)

### ⚙️ Key Technologies

This project uses a mix of data analysis and NLP-focused tools:

* Python for end-to-end processing.

* Libraries:

  * pandas, numpy for data manipulation and analysis.

  * transformers (Hugging Face) for fine-tuning pre-trained models (BERT).

  * matplotlib, seaborn for performance visualizations.

  * sklearn for classification evaluation metrics.

[View Code](https://github.com/soumya-thoutam/Data-Driven-Reasoning-Analysis/blob/main/A_NLP_ARC.ipynb)

### 🏗️ Project Structure

1. Data Collection: Retrieved the ARC dataset for processing.

   

2. Preprocessing: Cleaned, tokenized, and transformed the data into embeddings.

3. Model Fine-Tuning: Adapted pre-trained BERT models to handle reasoning-based questions.

   

4. Evaluation: Assessed model accuracy and identified areas for improvement.

### 🔍 Insights from the Project

**1. Data Preprocessing:**

  * Cleaned and tokenized unstructured text data from the ARC dataset.

  * Used embeddings to transform text into formats compatible with NLP models.

**2. Model Fine-Tuning:**

  * Fine-tuned the BERT transformer model to enhance performance on question-answering tasks.

  * Applied techniques like data augmentation and regularization to address issues like data imbalance and overfitting.

**3. Analysis of Results:**

  * Achieved meaningful performance improvements on the ARC Challenge dataset, demonstrating the potential of NLP for reasoning tasks.

  * Conducted error analysis to identify and understand the model's limitations in interpreting complex logical relationships.

**4. Challenges Overcome:**

  * Addressed dataset imbalance between the "Easy" and "Challenge" subsets, optimizing model fairness and robustness.

  * Uncovered key limitations of reasoning models, with a focus on the difficulty of handling abstract reasoning and inference-based questions.

### 🏁 Results and Evaluation

* Visualized key insights using Python libraries, including performance metrics and error trends.

* Proposed improvements for better accuracy in reasoning tasks, particularly in handling complex logic.

### 🌟 Project Demonstrates

* **Analytical Thinking:** Solving complex problems using a structured approach.

* **Data Handling:** Expertise in managing and preprocessing unstructured datasets.

* **Model Evaluation:** Interpreting results and refining workflows for better performance.

* **Visualization:** Presenting insights effectively using data visualization tools to communicate findings.

### 📄 Publication

* **Medium Article:**\

Read a high-level overview of this project and its findings [here](https://soumya-thoutam.medium.com/data-driven-reasoning-analysis-680879d37064).