Uncovering Uncertainty in Transformer Inference
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929619861504000 |
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| author | Brothers, Greyson Mannering, Willa Tien, Amber Winder, John |
| author_facet | Brothers, Greyson Mannering, Willa Tien, Amber Winder, John |
| contents | We explore the Iterative Inference Hypothesis (IIH) within the context of transformer-based language models, aiming to understand how a model's latent representations are progressively refined and whether observable differences are present between correct and incorrect generations. Our findings provide empirical support for the IIH, showing that the nth token embedding in the residual stream follows a trajectory of decreasing loss. Additionally, we observe that the rate at which residual embeddings converge to a stable output representation reflects uncertainty in the token generation process. Finally, we introduce a method utilizing cross-entropy to detect this uncertainty and demonstrate its potential to distinguish between correct and incorrect token generations on a dataset of idioms. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_05768 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncovering Uncertainty in Transformer Inference Brothers, Greyson Mannering, Willa Tien, Amber Winder, John Computation and Language Artificial Intelligence 68T50 (Primary), 68T07 (Secondary) F.2.2; I.2.7 We explore the Iterative Inference Hypothesis (IIH) within the context of transformer-based language models, aiming to understand how a model's latent representations are progressively refined and whether observable differences are present between correct and incorrect generations. Our findings provide empirical support for the IIH, showing that the nth token embedding in the residual stream follows a trajectory of decreasing loss. Additionally, we observe that the rate at which residual embeddings converge to a stable output representation reflects uncertainty in the token generation process. Finally, we introduce a method utilizing cross-entropy to detect this uncertainty and demonstrate its potential to distinguish between correct and incorrect token generations on a dataset of idioms. |
| title | Uncovering Uncertainty in Transformer Inference |
| topic | Computation and Language Artificial Intelligence 68T50 (Primary), 68T07 (Secondary) F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2412.05768 |