Enhancing Large Language Model Performance with Reinforcement Learning from Human Feedback: A Comprehensive Study on Q&A, Summarization, and Classification

Reinforcement Learning from Human Feedback (RLHF) has shown great potential in enhancing the alignment of Large Language Models (LLMs) with human preferences. In this study, we introduce a effective approach aimed at improving the performance of LLMs in tasks such as question-answering, Summarization, and classification. Our methodology incorporates RLHF into LLMs, facilitating better adherence to human expectations and resulting in superior task performance. We detail the implementation of RLHF within the LLM architecture, with a focus on leveraging reward signals derived from human feedback to direct model updates. This methodology encompasses data generation, Supervised Finetuning, reward model training, and reinforcement learning with Proximal Policy Optimization (PPO). Experimental results demonstrate that our proposed RLHF-augmented LLMs outperform baseline models, attesting to their efficacy in achieving higher performance while maintaining human-aligned behaviour. Notably, we observe a 5 to 15 percent improvement in outcomes using the RLHF implementation in LLMs. The findings of this study have significant implications for the advancement of more accurate, efficient, and human-centric language models.