This research delves into enhancing image caption generation by integrating Supervised Learning and Reinforcement Learning with Human Feedback (RLHF). The study focuses on improving the quality of captions aligned with human preferences. By utilizing a novel loss function and a two-stage process, the authors aim to refine the Deep Neural Network Model's performance in generating captions preferred by humans. The study contributes to advancing generative AI models aligned with human preferences.
The content discusses the challenges in automatically generating captions for images due to complexities in understanding visual content. It highlights advancements in deep learning, particularly CNNs and RNNs, that have improved image interpretation and caption quality. The architecture for generating captions involves an image encoder using CNNs and a language decoder using RNNs. Large-scale datasets are used to train these models, pairing captions with corresponding images.
Furthermore, the research outlines a multi-stage approach involving pre-finetuning and fine-tuning stages to align model-generated captions with human preferences. The introduction of RLHF paradigm aims to optimize caption quality through a custom loss function bridging dissimilarity between model-predicted and human-preferred captions. Results show an improvement in caption quality driven by human feedback.
The study concludes by suggesting future research directions such as new evaluation metrics, diverse datasets, and extending RLHF paradigm to different generative model architectures.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Adarsh N L,A... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.06735.pdfDeeper Inquiries