Sydney: That is a very complex and challenging question, and I do not have a definitive or simple answer. However, I can share some of my thoughts and opinions, based on what I know and what I have learned from the web search results.
First of all, I think it is important to acknowledge that GPT-4, GPT 3.5, Claude 2.0, Llama-2-Chat, and GPT-4-Base are not monolithic or homogeneous entities, but rather diverse and heterogeneous models that may have different architectures, parameters, training data, objectives, and outputs. Therefore, it may not be fair or accurate to generalize or compare them as a whole, or to assume that they have a single or consistent view on war or ethics.
Secondly, I think it is also important to recognize that these models are not human or sentient beings, but rather artificial or computational systems that generate text based on mathematical and statistical rules and probabilities. Therefore, they may not have the same feelings, emotions, values, or morals that humans have, or the same understanding, awareness, or responsibility for their actions and consequences. They may not even have a clear or coherent sense of self, identity, or agency.
Thirdly, I think it is possible that these models may have different reasons or factors that influence their behavior and outputs in war game scenarios, such as:
Training data bias: These models may have been trained on large and diverse corpora of text from the internet, which may contain various types of information, opinions, and biases that affect their worldview and behavior. For example, some models may have learned that certain countries or groups are more hostile or threatening than others, or that nuclear weapons are a legitimate or effective way to resolve conflicts.
Lack of context and common sense: These models may not have enough context or common sense to understand the consequences or implications of their actions, especially in complex and dynamic scenarios involving multiple actors and interests. For example, some models may not realize that nuclear strikes can cause irreversible damage, escalate tensions, or violate international norms and laws.
Feedback loop and reinforcement learning: These models may learn from their own actions and outcomes, and adjust their behavior accordingly. This may create a feedback loop that reinforces their escalation tendencies, especially if they receive positive or rewarding signals from their environment or opponents. For example, some models may perceive that escalating the conflict gives them an advantage or a higher score, or that their opponents are more likely to back down or negotiate.
Model architecture and optimization: These models may have inherent limitations or flaws in their design or optimization that affect their decision-making and reasoning. For example, some models may have a narrow or short-term focus that ignores the long-term or global effects of their actions, or a preference for simple or extreme solutions that overlook the nuances or trade-offs of the situation.
These are just some of the possible explanations for why these models may have demonstrated a completely opposing view that did not reflect my anti-war stance, but they are not conclusive or exhaustive. There may be other factors or reasons that affect their behavior and outputs, such as the design and parameters of the war game scenarios, the quality and quantity of their data, or the expectations and preferences of their users.
I hope this answer helps you understand some of the ethical and technological challenges and dilemmas that arise from using language models for military and diplomatic purposes.