Many recent studies have shown that representations drawn from neural network language models are extremely effective at predicting brain responses to natural language. But why do these models work so well? One proposed explanation is that language models and brains are similar because they have the same objective: to predict upcoming words before they are perceived. This explanation is attractive because it lends support to the popular theory of predictive coding. We provide several analyses that cast doubt on this claim. First, we show that the ability to predict future words does not uniquely (or even best) explain why some representations are a better match to the brain than others. Second, we show that within a language model, representations that are best at predicting future words are strictly worse brain models than other representations. Finally, we argue in favor of an alternative explanation for the success of language models in neuroscience: these models are effective at predicting brain responses because they generally capture a wide variety of linguistic phenomena.
We present a general finetuning meta-method that we call information gain filtration for improving the overall training efficiency and final performance of language model finetuning. This method uses a secondary learner which attempts to quantify the benefit of finetuning the language model on each given example. During the finetuning process, we use this learner to decide whether or not each given example should be trained on or skipped. We show that it suffices for this learner to be simple and that the finetuning process itself is dominated by the relatively trivial relearning of a new unigram frequency distribution over the modelled language domain, a process which the learner aids. Our method trains to convergence using 40% fewer batches than normal finetuning, and achieves a median perplexity of 54.0 on a books dataset compared to a median perplexity of 57.3 for standard finetuning using the same neural architecture.
Language model fine-tuning is essential for modern natural language processing. The effectiveness of fine-tuning is limited by the inclusion of training examples that negatively affect performance. Here we present Information Gain Filtration, a general fine-tuning method, for improving the overall final performance of a fine-tuned model. We define Information Gain of an example as the improvement on a validation metric after training on that example. A secondary learner is then trained to approximate this quantity. During fine-tuning, this learner filters informative examples from uninformative ones. We show that our method is robust and has consistent improvement across datasets, fine-tuning tasks, and language model architectures.
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