Enhancing Question Generation with Novel Reward Functions: Evaluation and Comparison

Overview

Improving the creation of questions by fine tuning a base LLM through Reinfocement Learning using new novel reward functions is the focus of "Enhancing Question Generation with Novel Reward Functions: Evaluation and Comparison." This study examines and compares these new approaches to see how well they work and what improvements they bring.

Architecture:

• Pretrained SQUADv2 model
• PPO (Proximal Policy Optimization) instead of SCST (Self-Critical Sequence Training)

Hyperparameters

• Batch Size: 512
• Total Batches: 50 out of 170
• Learning Rate: 5e-5
• Generation Kwargs: "min_new_tokens": 1, "max_new_tokens": 32

Dataset :

• https://pytorchnlp.readthedocs.io/en/latest/_modules/torchnlp/datasets/squad.html

Reference Paper :

• https://dl.acm.org/doi/pdf/10.1145/3471158.3472240