Imagine training multiple AI models on the same tabular dataset – like predicting loan approvals or diagnosing diseases. You'd expect similar results, right? Not quite. A fascinating phenomenon called "fine-tuning multiplicity" reveals that these models, despite achieving similar overall accuracy, can make wildly different predictions for the same individual. This raises serious questions about the reliability of AI, especially in high-stakes domains. The problem arises from the complex training process of large language models (LLMs) on limited tabular data. Variations in random initialization, seed selection, or even slight changes in data can lead to these "multiplicity" issues. Researchers are exploring ways to quantify and address this challenge. One promising approach involves assessing the "consistency" of predictions by examining the model's behavior within the local neighborhood of an input in the embedding space. Essentially, the method checks if the AI's confidence in its predictions remains stable even with slight perturbations in the data. This "consistency" measure gives us a probabilistic guarantee of robustness. If a prediction is highly consistent, we can be more confident that it will hold true across different versions of the model. This research is crucial for building trustworthy AI. Imagine an AI model that predicts loan approval – a high consistency score would give us more confidence that the decision is robust, not just an artifact of the specific training process. While understanding and measuring multiplicity is a significant step, more work is needed to mitigate this issue. Future research could focus on developing techniques to enforce consistency across different model versions, leading to more predictable and trustworthy AI for real-world applications.