Is it Language or Task Design? (OFAI Lecture by Jordan Kodner)
Channel: Austrian Research Institute for AI (OFAI)
Date: 24/04/2024

Is it Language or Task Design? Reinterpreting language models' recent successes in morphology and syntax learning. 
Jordan Kodner, PhD, Stony Brook University 
24 April 2024

The success of neural language models (LMs) on a wide range of language-related tasks may be in part due to their ability to induce human-like representations or understanding of natural language grammars. Humans are, after all, gold-standard language learners. For the past several years, researchers pursuing this question have developed a number of methodologies for testing the grammar representations learned by LMs that have reached generally positive conclusions. I will take a critical look at such studies in this talk. While modern LMs are clearly extremely impressive, and clearly do often capture important aspects of natural language grammars, the methodologies of many popular studies have unfairly overestimated the capacities of LMs when it comes to their ability to induce human-like representations. Focusing on questions of hierarchical syntactic representations and generalization in inflectional morphology, I will discuss how unintended biases in data-splitting, artificial training or test data, overly simplistic evaluations, weak or absent baselines, and faulty interpretations, have conspired to overestimate the abilities of LMs. While the conclusions of this study are largely negative in terms of the current state-of-affairs, they are also optimistic. By employing more thorough and rigorous methodologies, we have developed a better scientific understanding of the nature of LMs and representations of the grammar. In identifying weak points for current models, we point towards research areas where greater improvements may be gained.