Prompt-Model Interaction Reaches the Fixed Points: A deterministic, task-free structural readout -- and the factorizations of it that failed

Imagine you're developing a language model that needs to understand and respond to various prompts effectively. You might think that optimizing a prompt for one model would yield similar results across others, but that's not the case. In practice, prompts that work well for one model can perform poorly on another, leading to unexpected outcomes. This inconsistency highlights a significant gap in our understanding of how prompts influence model behavior, especially when the task isn't clearly defined. The authors explore this by analyzing a specific readout mechanism in models, focusing on how a short sequence of tokens can dramatically shift model performance. They find that while certain conditioning factors can influence outcomes, many proposed explanations fail to hold up under scrutiny. This suggests that the relationship between prompts and models is more complex than previously thought. For builders, this means that relying on prompt optimization alone may not be sufficient; understanding the underlying mechanics of model interactions is crucial for developing robust AI systems.

The paper challenges existing assumptions about prompt effectiveness across models.

The findings are based on empirical observations but lack extensive baseline comparisons.