Socratic Question Generation

Requirements

This codebase requires pytorch and huggingface to be installed which can be installed using pip.

Generate Invalid questions

python generate_bad_questions.py
python classifiy_gen_questions.py
python filter_bad_questions.py
python create_preference_dataset.py

The invalid questions are stored in bad_questions.json. The classification results are stored in classifiy_gen_questions.json. The correct invalid questions are stored in valid_bad_questions.json. The preference dataset is stored in create_preference_dataset.py.

Preference Optimization

python finetune/sft_dpo.py

This code takes several arguments which can be seen using the -h flag. --sft flag corresponds to standard fine-tuning and --dpo corresponds to direct preference optimization.

Some example commands include:

1. Standard Fine-Tuning

python finetune/sft_dpo.py --sft --base_model codellama/CodeLlama-7b-Instruct-hf --model_name codellama_sft_b2 --batch_size 2 --grad_accum_steps 32 --epochs 5

2. DPO

python finetune/sft_dpo.py --dpo --base_model codellama/CodeLlama-7b-Instruct-hf --model_name codellama_sft_b2 --pt_model_name codellama_sft_b2 --batch_size 1 --grad_accum_steps 64 --epochs 2

3. Generate

python finetune/sft_dpo.py --generate --model_name codellama_sft_b2_dpo --pt_model_name codellama_sft_b2 --decoding greedy

LLama Zero-Shot Experiments

python zero-shot_llama_prompt.py 

This code takes an argument --prompt cot for chain-of-thought prompting.

Evaluate

python evaluate.py --result_file <path_to_results_file>

Additionally, the code takes two arguments --zero True and --cot True for LLama zero-shot and CoT respectively.