Calibration, Entropy Rates, and Memory in Language Models
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.
(emergency review) This paper demonstrates that a left-to-right language model suffers a high entropy rate when generating a long-term sequence of words. Then the authors claim that this is because of entropy rate amplification, which could be mitigated by *calibration*. With local entropy rate calibration, a language model could achieve lower perplexity generating shorter and concise sequences of words. The proposed technique (local entropy rate calibration) is straightforward to implement, and empirically shown to be effective. This would be easily applied to the decoder in many seq2seq models, expected to improve various language generation tasks. However, other language models that use bi-directional connections (BERT, RoBERTa, ALBERT) or GAN based language generation models are omitted, and I think these models should be considered to make this work have more impact.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Calibration, Entropy Rates, and Memory in Language Models
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.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



(emergency review) This paper demonstrates that a left-to-right language model suffers a high entropy rate when generating a long-term sequence of words. Then the authors claim that this is because of entropy rate amplification, which could be mitigated by *calibration*. With local entropy rate calibration, a language model could achieve lower perplexity generating shorter and concise sequences of words. The proposed technique (local entropy rate calibration) is straightforward to implement, and empirically shown to be effective. This would be easily applied to the decoder in many seq2seq models, expected to improve various language generation tasks. However, other language models that use bi-directional connections (BERT, RoBERTa, ALBERT) or GAN based language generation models are omitted, and I think these models should be considered to make this work have more impact.
assistant
(emergency review) This paper demonstrates that a left-to-right language model suffers a high entropy rate when generating a long-term sequence of words. Then the authors claim that this is because of entropy rate amplification, which could be mitigated by *calibration*. With local entropy rate calibration, a language model could achieve lower perplexity generating shorter and concise sequences of words. The proposed technique (local entropy rate calibration) is straightforward to implement, and empirically shown to be effective. This would be easily applied to the decoder in many seq2seq models, expected to improve various language generation tasks. However, other language models that use bi-directional connections (BERT, RoBERTa, ALBERT) or GAN based language generation models are omitted, and I think these models should be considered to make this work have more impact.