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arXiv · 2106.08367

What Context Features Can Transformer Language Models Use?

Joe O'Connor·Jacob Andreas
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Abstract

Transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. What aspects of these contexts contribute to accurate model prediction? We describe a series of experiments that measure usable information by selectively ablating lexical and structural information in transformer language models trained on English Wikipedia. In both mid- and long-range contexts, we find that several extremely destructive context manipulations -- including shuffling word order within sentences and deleting all words other than nouns -- remove less than 15% of the usable information. Our results suggest that long contexts, but not their detailed syntactic and propositional content, are important for the low perplexity of current transformer language models.

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Joe O'Connor, Jacob Andreas. 2021-06-15. What Context Features Can Transformer Language Models Use?. https://arxiv.org/abs/2106.08367

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