Socrates LLM

About the Project

Inspiration

The inspiration for this project came from the growing need to leverage large language models (LLMs) for educational purposes, especially in facilitating Socratic dialogue. By generating intelligent question-answer pairs, LLMs can encourage deeper learning and critical thinking across diverse subjects. This project was designed to automate and optimize the process of creating, evaluating, and fine-tuning such models for specific educational topics, like Data Structures and Algorithms.

What We Learned

Throughout the project, we explored several key areas of machine learning and NLP, including:

• Socratic Questioning: Understanding how LLMs can be guided to generate Socratic dialogue, leading to more interactive and meaningful learning experiences.
• Evaluation Metrics: Utilizing BLEU, ROUGE, and other metrics to evaluate the performance of language models on text generation tasks.
• Fine-Tuning: Hands-on experience with fine-tuning pre-trained models (such as GPT-2) to adapt them to specialized datasets, improving their output quality in a focused domain.

How We Built It

The project is composed of three core components:

1. Dataset Generation: We use OpenAI's API to generate Socratic question-answer pairs on a selected topic, saving the output in JSONL format.

2. Evaluation Metrics: A custom evaluation pipeline was built to score the generated responses using word overlap, BLEU, ROUGE, and TF-IDF similarity. These metrics help to assess the quality and relevance of the model's output.

3. Fine-Tuning the LLM: Finally, we fine-tune a pre-trained LLM (like GPT-2) using the generated dataset. This step enhances the model’s ability to generate Socratic dialogue on a specific topic, making it more accurate and aligned with educational goals.

Challenges We Faced

Building the project involved several challenges:

• API Rate Limits: While generating large datasets, we encountered rate limits with the OpenAI API, which required us to optimize the number of concurrent tasks and handle errors gracefully.

• Evaluation Complexity: Evaluating text generation is inherently subjective, and choosing the right set of metrics was critical. We had to balance between lexical overlap (BLEU, ROUGE) and semantic similarity (TF-IDF) to get a well-rounded evaluation.

• Fine-Tuning: Fine-tuning models can be resource-intensive. Managing GPU usage and training times, while ensuring the model effectively learned from the new data, required careful optimization.

Built With

• asyncio-libraries:-openai-api
• fastapi
• openai
• openaid
• pandas
• scikit-learn
• tqdm