Exploring challenges and approaches for ChatGPT in multilingual and multisectoral contexts

Abstract

ChatGPT, a transformer-based chatbot model, has gained significant attention for its ability to generate natural and coherent responses. However, the model still faces several challenges that limit its performance and applicability. This essay explores the current challenges of ChatGPT and proposes solutions to overcome them. The challenges identified include the scarcity of diverse and high-quality training data, coherence and topic transition issues in long conversations, and the risk of overfitting during training. To address these challenges, the essay proposes several solutions. Transfer learning techniques are suggested to improve model generalization by pre-training on a large corpus and fine-tuning on specific chatbot tasks. Regularization methods such as dropout and weight decay are recommended to prevent overfitting and improve generalization. The design of more effective evaluation metrics, including F1 score and human evaluation, is proposed to accurately assess the model's performance. Additionally, incorporating contextual information from previous conversation turns is explored to enhance coherence.The proposed solutions are evaluated through experiments and benchmarking. The results demonstrate promising improvements in the performance of ChatGPT, including enhanced coherence, better topic transition, reduced overfitting, and higher-quality generated responses. This research contributes to the advancement of chatbot models by addressing the challenges faced by ChatGPT. The proposed solutions offer practical strategies to improve the model's performance and applicability in real-world scenarios. The findings have implications for various industries that rely on chatbot technology, enabling them to provide more natural and coherent interactions with users.