Reconsidering the Past: Optimizing Hidden States in Language Models
This incremental improvement addresses efficiency and adaptability for users of pretrained language models in inference and few-shot learning scenarios.
The paper tackles the problem of improving transformer language model performance at inference time by introducing Hidden-State Optimization (HSO), a gradient-based method that updates cached hidden states instead of model parameters, resulting in reduced perplexity on datasets like WikiText103 and PG-19, especially out-of-distribution, and gains in few-shot evaluation without extra parameters or data.
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.