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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21473 |
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| _version_ | 1866908559144386560 |
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| author | Liu, Hude Hu, Jerry Yao-Chieh Zhang, Jennifer Yuntong Song, Zhao Liu, Han |
| author_facet | Liu, Hude Hu, Jerry Yao-Chieh Zhang, Jennifer Yuntong Song, Zhao Liu, Han |
| contents | We formalize hallucinations in generative models as failures to link an estimate to any plausible cause. Under this interpretation, we show that even loss-minimizing optimal estimators still hallucinate. We confirm this with a general high probability lower bound on hallucinate rate for generic data distributions. This reframes hallucination as structural misalignment between loss minimization and human-acceptable outputs, and hence estimation errors induced by miscalibration. Experiments on coin aggregation, open-ended QA, and text-to-image support our theory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21473 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are Hallucinations Bad Estimations? Liu, Hude Hu, Jerry Yao-Chieh Zhang, Jennifer Yuntong Song, Zhao Liu, Han Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition We formalize hallucinations in generative models as failures to link an estimate to any plausible cause. Under this interpretation, we show that even loss-minimizing optimal estimators still hallucinate. We confirm this with a general high probability lower bound on hallucinate rate for generic data distributions. This reframes hallucination as structural misalignment between loss minimization and human-acceptable outputs, and hence estimation errors induced by miscalibration. Experiments on coin aggregation, open-ended QA, and text-to-image support our theory. |
| title | Are Hallucinations Bad Estimations? |
| topic | Machine Learning Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.21473 |