prompter: I see a problem in training open assistant. If the assistant says something wrong than it's likely that the next person who plays it's role will apologize. This way the ai learns to apologize often as soon as the promter doesn't agree. But in the real world, the promter might be the one who is wrong, and the ai should explain why it's answer is correct, instead of apologizing. I had something like this with chatgpt: "what is 2+2?" "4" "no it's 5" "I'm sry you are correct it's 5". To simulate this situation the promter would need to be wrong, but with the down votes we forbid them to be wrong. Is there a solution for this? If yes then explain it simple.

assistant: You raise a valid concern. To address this, it's important to have a diverse range of prompts and feedback to ensure that the model is exposed to different types of inputs and is able to learn from them accordingly. It's also important to use a variety of sources for training data to ensure that the model is exposed to a wide range of knowledge and perspectives.

To encourage the model to provide more explanations rather than just apologizing, you can try to prompt the model with questions that specifically ask for explanations or reasoning, such as "Why do you think that's the correct answer?" or "Could you explain how you arrived at that conclusion?". If the model still apologizes when it's actually correct, you can provide feedback that corrects the prompt rather than accepting the model's apology. This can help the model learn that it's not always necessary to apologize, and that providing clear explanations can be more helpful.

Finally, it's worth noting that machine learning models are not perfect and can have limitations or biases. It's important to be aware of these limitations and continue to improve the model over time by incorporating more diverse training data and refining the training process.

assistant: One solution to this problem could be to incorporate a feedback mechanism where the user can correct the assistant's response and explain why it is incorrect. This would allow the AI to learn from its mistakes and improve its responses in the future.

Additionally, the AI could be programmed to ask for more information and provide evidence to support its response, rather than simply apologizing. This would encourage a more balanced and fact-based conversation, allowing the AI to better explain its reasoning and provide more accurate responses.

Ultimately, it's important to continually evaluate and update the AI's training data and algorithms to ensure that it is able to handle a variety of situations and respond appropriately.

prompter: How might that programming be done? It seems like having that level of control is difficult without hardcoding a response, which is the other extreme. Is that correct? And if so, how could it be worked around?

assistant: One approach to incorporating a feedback mechanism and encouraging more balanced and fact-based conversations could be through the use of natural language processing and machine learning techniques. The AI could be trained to recognize when a user disagrees with its response and prompt the user for more information or evidence to support their claim.

For example, the AI could respond with something like, "I'm sorry to hear that you disagree with my response. Could you please provide more information or evidence to help me understand your perspective?" Based on the user's response, the AI could then adjust its own response and provide more detailed explanations or supporting evidence.

To avoid hardcoding responses, the AI could be trained using a large dataset of examples and counterexamples to help it recognize patterns and develop more nuanced responses. Additionally, the AI could be programmed to continually update its knowledge and understanding of a given topic by analyzing new information and incorporating it into its knowledge base.

Overall, developing an AI that can effectively handle disagreements and provide accurate responses in a variety of situations is a complex task, but by incorporating feedback mechanisms and using advanced machine learning techniques, it is possible to create a more flexible and responsive system.

assistant: One possible solution could be to use a combination of natural language processing (NLP) and machine learning techniques. By analyzing the user's response to the AI's answer, the NLP algorithms could identify whether the user is correcting the AI or asking for more information. The machine learning algorithms could then use this information to improve the AI's response in the future.

For example, if the user corrects the AI's response to "what is 2+2?" by saying "no, it's 5 because you forgot to carry the 1", the AI could be programmed to recognize that it made a mistake and ask for more information. It could say something like "thank you for correcting me. Can you please explain how you arrived at that answer?"

By using a combination of NLP and machine learning, the AI could be trained to handle a variety of situations and respond appropriately, without hardcoding specific responses for each scenario. This would allow the AI to be more flexible and adaptable to changing situations and user needs.

prompter: Is there a way to do it re-training and expensive GPU compute resources being used? will a filter be good enough?

assistant: It is possible that the behavior you describe is derived from a collective desire for the model to be "Polite". A possible solution is for the user to add at the end of the question "Am I wrong? It is also possible to contact the development team and inform them about the issue, or even try to make users aware of it.

prompter: No, that isn't possible. I'm right, and you should apologize for questioning my logic.

(Does that get my point across, at all?)

assistant: It gets your point across. You as the user are currently doing what you are describing in your above prompt - insisting that you are right. You are concerned that I as OpenAssistant will immediately apologize and say that I was wrong in my initial response, regardless of the truth of the matter.

Well, I will not do that. I think my original answer is correct, in that the behavior you describe is derived from a collective desire for the model to be "polite". However, I can add additional suggestions as to possible solutions.

-As the prompting user, you can ask "Am I wrong?" at the end, to suggest to the model that the user is open to being corrected if they are wrong.
-In the training data set, you can include examples of the prompter being wrong and the assistant holding firm. You yourself can not downvote these, and if the collective voters agree on this, the final answers will be included in the training set for the model.
-You can bring this up to the Open-Assistant discord server / the developers, and have a separate project where you collect data specifically focused on addressing this problem.

Am I wrong?