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- W3035131109 abstract "Building accurate language models that capture meaningful long-term dependencies is a core challenge in natural language processing. Towards this end, we present a calibration-based approach to measure long-term discrepancies between a generative sequence model and the true distribution, and use these discrepancies to improve the model. Empirically, we show that state-of-the-art language models, including LSTMs and Transformers, are emph{miscalibrated}: the entropy rates of their generations drift dramatically upward over time. We then provide provable methods to mitigate this phenomenon. Furthermore, we show how this calibration-based approach can also be used to measure the amount of memory that language models use for prediction." @default.
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- W3035131109 date "2020-07-12" @default.
- W3035131109 modified "2023-09-24" @default.
- W3035131109 title "Calibration, Entropy Rates, and Memory in Language Models" @default.
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