Shared Imagination: Llms Hallucinate Alike

Zhou Yilun, Xiong Caiming, Savarese Silvio, Wu Chien-sheng. Arxiv 2024

[Paper]    
Applications Efficiency And Optimization Model Architecture Prompting Training Techniques

Despite the recent proliferation of large language models (LLMs), their training recipes – model architecture, pre-training data and optimization algorithm – are often very similar. This naturally raises the question of the similarity among the resulting models. In this paper, we propose a novel setting, imaginary question answering (IQA), to better understand model similarity. In IQA, we ask one model to generate purely imaginary questions (e.g., on completely made-up concepts in physics) and prompt another model to answer. Surprisingly, despite the total fictionality of these questions, all models can answer each other’s questions with remarkable success, suggesting a “shared imagination space” in which these models operate during such hallucinations. We conduct a series of investigations into this phenomenon and discuss implications on model homogeneity, hallucination, and computational creativity.

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