To address the limitations of GPT-4 in reducing redundancy and self-contradiction, several model refinements and architectural changes can be implemented. First, enhancing the training dataset with diverse examples that explicitly illustrate redundancy and self-contradiction can help the model learn to identify and avoid these issues. This could involve curating a dataset that includes dialogues with intentional redundancies and contradictions, allowing the model to learn from both correct and incorrect examples.
Second, incorporating attention mechanisms that focus on context and previous turns in the dialogue can improve coherence and reduce redundancy. By refining the model's ability to track conversational history, it can better understand when information has already been presented, thus minimizing repetitive responses.
Third, implementing a feedback loop where the model receives real-time evaluations of its responses could help it learn from its mistakes. This could involve using reinforcement learning techniques, where the model is rewarded for generating responses that are coherent and free from redundancy or contradictions.
Lastly, exploring architectural changes such as integrating memory networks could enhance the model's ability to retain context over longer dialogues. This would allow GPT-4 to reference earlier parts of the conversation more effectively, reducing the likelihood of self-contradiction and redundancy.