| --- |
| license: mit |
| --- |
| |
| # SQL Socratic Models |
|
|
| This repository contains fine-tuned large language models for **Socratic SQL instruction** in higher education, focusing on guiding learners through SQL concepts using structured reasoning rather than providing direct solutions. |
|
|
| ## Models |
| - phi3_rq4 |
| - qwen25 |
| - gemma2 |
| |
| ## Method |
| |
| Our approach is designed to support **conceptual learning in STEM education** through Socratic interaction: |
| |
| - **Phase 1 (Data Construction):** |
| SQL instruction data is augmented with Socratic prompts emphasizing: |
| - Question decomposition |
| - Conceptual hints |
| - Guided reasoning steps |
| |
| - **Phase 2 (Fine-Tuning):** |
| We apply full fine-tuning (FFT) on small, open-source LLMs with **pedagogical constraints** that explicitly discourage direct answer generation and instead promote: |
| - Conceptual scaffolding |
| - Incremental reasoning |
| - Learner-centered guidance |
| |
| - **Phase 3 (Evaluation):** |
| Models are evaluated using: |
| - **BERTScore** for semantic alignment with expected reasoning |
| - **ROUGE-L** to measure and control **answer leakage** (i.e., avoidance of direct SQL solutions) |
| |
| ## Contributions |
| - Fine-tuning across multiple architectures (Phi-3, Qwen2.5, Gemma2) for **instructional SQL reasoning** |
| - Development of **Socratic SQL prompting framework** for higher education contexts |
| - Evaluation of models on their ability to generate **guidance without revealing final answers** |
| - Ablation study identifying factors that enable LLMs to mimic effective instructors through: |
| - Misconception-aware feedback |
| - Iterative questioning |
| - Structured reasoning support |
| |
| ## Task |
| |
| Given a natural language SQL question, the model generates: |
| |
| 1. Socratic reasoning steps |
| 2. Conceptual hints and guiding questions |
| 3. Intermediate decomposition of the problem |
| |
| **The model does NOT produce the final SQL query**, ensuring alignment with instructional use in higher education settings. |
| |
| This design supports: |
| - Active learning |
| - Conceptual understanding of SQL |
| - Integration of database concepts into broader STEM curricula |
| |
| ## Usage |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained("sriram882004/SQL-Socratic-Models/phi3_rq4") |
| tokenizer = AutoTokenizer.from_pretrained("sriram882004/SQL-Socratic-Models/phi3_rq4") |
