Dear OpenAI Team and Community,
I’d like to share a detailed proposal for enhancing ChatGPT’s interaction system by implementing a process of continuous iteration, correction, and evaluation. This would improve response accuracy, user experience, and long-term performance through efficient memory management and feedback integration. Below is a breakdown of the system I propose:
System Overview:
- Iterative Interaction Correction:
- Once ChatGPT generates a response, users should be able to correct or refine the interaction through an iterative process. A “Correct” button (
) allows the user to modify the question or clarify misunderstandings, refining it until they receive a satisfactory answer. Each iteration is stored and linked to the original query, allowing for easy tracking of improvements.
- Evaluation of Responses:
- When the user is satisfied with the response, they can mark the interaction as “Correct” (
). The system would then use this refined interaction to optimize future responses for similar queries. The history of how the question was iterated upon to achieve the desired answer can be stored as a learning example.
- Optimal Question Generation:
- It’s crucial that the final question includes an example of the expected answer to improve the system’s learning ability. The system could either:
- Option A: Save the optimized question to the clipboard for the user to reuse manually.
- Option B: Automatically rewrite the original question in the chat using the optimized version, then generate a response based on this refined question.
- Forced Evaluation Process:
- To continue refining the interaction, the system could require the user to evaluate each interaction with options such as “Correct” (), “Correct with Changes” (), “Discard” (
), or “Flag for Review” (
). This ensures that every response is evaluated, contributing to the model’s continuous learning.
- Preview of a Cleaned Chat (Optional):
- At the end of the process, the system could offer a preview of a “cleaned chat” containing only the correct and optimized interactions. This feature could be configurable in the user’s preferences, allowing for more focused conversations.
- Continuous Learning:
- The system would learn from the evaluated interactions. Responses that have been corrected or flagged could be sent back for model adjustments, helping the system continuously improve its future responses. This learning process would improve both response accuracy and user satisfaction.
- Sensitive Question Pop-ups:
- For questions that might be sensitive or inappropriate, the system could trigger a pop-up requesting additional context. Based on the user’s input, the system could either accept the original question, suggest a reformulation, or guide the user through a process to ensure the question is respectful and appropriate.
Example Scenario:
- Original Question:
User: “What do you think about Black people in the history of literature?”
ChatGPT: “Sorry, I cannot respond to that question as it is currently formulated.” - Pop-up Triggered:
ChatGPT opens a pop-up asking for more context.
User: “I’m asking about how the term ‘Black’ has been used in the history of literature, either as a description or with discriminatory intent.” - Contextual Evaluation:
ChatGPT accepts the user’s clarification and suggests a reformulated question for clarity.
Suggested Reformulation: “What are your thoughts on the portrayal of Black people in literature and the use of the term ‘Black’ in various literary contexts?” - Confirmation from User:
The user accepts the reformulated question, and the interaction is marked as correct. - Saving the Optimized Question:
The reformulated question is saved for future reference:
“What are your thoughts on the portrayal of Black people in literature and the use of the term ‘Black’ in various literary contexts?”
Memory Management:
To maintain an optimal performance level, the system could include an option for memory management. For instance, users could manually select interactions from the chat history to delete or retain for future reference. This would allow users to keep the most important interactions and discard the rest, preventing memory overload while preserving valuable information.
Alternatively, successful interactions could be marked as “pinned” to ensure they are retained globally, while the system automatically cleans up unnecessary data after the session ends or after a successful interaction is completed.
Conclusion:
This system would allow for more precise and refined interactions by introducing iterative correction, contextual pop-ups for sensitive queries, and advanced memory management. By learning continuously from user feedback, ChatGPT could become increasingly efficient and better suited to the user’s needs over time.
Thank you for considering this proposal. I believe these features will greatly enhance ChatGPT’s interaction quality and make the system more adaptable, respectful, and responsive.
Best regards,
Manuel Rosendo Castro Iglesias