Shared Imagination: LLMs Hallucinate Alike
<|reference_start|>Shared Imagination: LLMs Hallucinate Alike: 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.<|reference_end|>
@article{zhou2024shared,
    title={Shared Imagination: LLMs Hallucinate Alike},
    author={Yilun Zhou, Caiming Xiong, Silvio Savarese, Chien-Sheng Wu},
    journal={arXiv preprint arXiv:2407.16604},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16604},
    primaryClass={cs.CL}
}