Enhancing Image Caption Generation Using Reinforcement Learning with
  Human Feedback
Adarsh N L Arun P V Aravindh N L
Research on generative models to produce human-aligned / human-preferred
outputs has seen significant recent contributions. Between text and
image-generative models, we narrowed our focus to text-based generative models,
particularly to produce captions for images that align with human preferences.
In this research, we explored a potential method to amplify the performance of
the Deep Neural Network Model to generate captions that are preferred by
humans. This was achieved by integrating Supervised Learning and Reinforcement
Learning with Human Feedback (RLHF) using the Flickr8k dataset. Also, a novel
loss function that is capable of optimizing the model based on human feedback
is introduced. In this paper, we provide a concise sketch of our approach and
results, hoping to contribute to the ongoing advances in the field of
human-aligned generative AI models.