Year: 2023
Type: dissertation
Abstract: Pretrained language models (LMs) have demonstrated a remarkable ability to emit linguistic and factual knowledge in certain fields. Additionally, they seem to encode relational information about different concepts in a knowledge base. However, since they are trained solely on textual corpora, it is unclear whether these models implicitly understand anything grounded about the real world. This work investigates the extent to which LMs learn the structure of the physical world. By probing the contextualized embeddings of sentences, we examine how well LMs predict the sizes of real-world objects. We further explore the effect of adjectival modifiers on object embeddings. We show that while larger models more accurately convey scalar information through their embeddings, they perform on par with smaller models in the task of contextual prediction. Fortunately, the models are capable of identifying a difference in scale when an adjectival modifier is introduced, implying that the relevant context is successfully incorporated into the object’s embedding through the LM’s attention mechanism.
Author: Vedantam, Saaketh
Institution: Massachusetts Institute of Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7014557847