Year: 2021
Type: conference-paper
Abstract: Neural language models exhibit impressive performance on a variety of tasks, but their internal reasoning may be difficult to understand.Prior art aims to uncover meaningful properties within model representations via probes, but it is unclear how faithfully such probes portray information that the models actually use.To overcome such limitations, we propose a technique, inspired by causal analysis, for generating counterfactual embeddings within models.In experiments testing our technique, we produce evidence that suggests some BERT-based models use a tree-distancelike representation of syntax in downstream prediction tasks.
Authors: Mycal Tucker, Peng Qian, Roger Levy
Institution: Massachusetts Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W3176175390