Across heavy NP shift, dative alternation and multiple PP shift, LLM ordering preferences correlate with human judgments, but particle movement preferences do not.
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
Recurrent neural networks (RNNs) are the state of the art in sequence modeling for natural language. However, it remains poorly understood what grammatical characteristics of natural language they implicitly learn and represent as a consequence of optimizing the language modeling objective. Here we deploy the methods of controlled psycholinguistic experimentation to shed light on to what extent RNN behavior reflects incremental syntactic state and grammatical dependency representations known to characterize human linguistic behavior. We broadly test two publicly available long short-term memory (LSTM) English sequence models, and learn and test a new Japanese LSTM. We demonstrate that these models represent and maintain incremental syntactic state, but that they do not always generalize in the same way as humans. Furthermore, none of our models learn the appropriate grammatical dependency configurations licensing reflexive pronouns or negative polarity items.
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Language Models Largely Exhibit Human-like Constituent Ordering Preferences
Across heavy NP shift, dative alternation and multiple PP shift, LLM ordering preferences correlate with human judgments, but particle movement preferences do not.