Reconsidering the Past: Optimizing Hidden States in Language Models

• URL: http://arxiv.org/abs/2112.08653v1
• Date: Thu, 16 Dec 2021 06:14:37 GMT
• Title: Reconsidering the Past: Optimizing Hidden States in Language Models
• Authors: Davis Yoshida and Kevin Gimpel
• Abstract summary: We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models. HSO computes the gradient of the log-probability the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters.
• Score: 35.7524942657169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Hidden-State Optimization (HSO), a gradient-based method for improving the performance of transformer language models at inference time. Similar to dynamic evaluation (Krause et al., 2018), HSO computes the gradient of the log-probability the language model assigns to an evaluation text, but uses it to update the cached hidden states rather than the model parameters. We test HSO with pretrained Transformer-XL and GPT-2 language models, finding improvement on the WikiText103 and PG-19 datasets in terms of perplexity, especially when evaluating a model outside of its training distribution. We also demonstrate downstream applicability by showing gains in the recently developed prompt-based few-shot evaluation setting, again with no extra parameters or training data.

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