Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

Abstract

A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge. We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.

Incremental Processing of Principle B: Mismatches Between Neural Models and Humans

“…1 Within work in natural language processing, existing models have been claimed to capture aspects of Principle A (e.g., Warstadt et al, 2020;Hu et al, 2020). Principle C has received less attention, though see Mitchell et al (2019) which found that LSTM language models failed to obey Principle C. Coreference, more broadly, has also been explored, with results suggesting that models encode features of coreference resolution (e.g., Sorodoc et al, 2020) and the interaction of implicit causality and pronouns (verb biases that influence preferred antecedents for pronouns; Upadhye et al, 2020;Davis and van Schijndel, 2021;Kementchedjhieva et al, 2021).…”