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|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 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.|
|Citation:||Braverman, Mark, Xinyi Chen, Sham Kakade, Karthik Narasimhan, Cyril Zhang, and Yi Zhang. "Calibration, Entropy Rates, and Memory in Language Models." In Proceedings of the 37th International Conference on Machine Learning (2020): pp. 1089-1099.|
|Pages:||1089 - 1099|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 37th International Conference on Machine Learning|
|Version:||Final published version. Article is made available in OAR by the publisher's permission or policy.|
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