Scope Ambiguities in Large Language Models
Gaurav Kamath Sebastian Schuster Sowmya Vajjala Siva Reddy
Sentences containing multiple semantic operators with overlapping scope often
create ambiguities in interpretation, known as scope ambiguities. These
ambiguities offer rich insights into the interaction between semantic structure
and world knowledge in language processing. Despite this, there has been little
research into how modern large language models treat them. In this paper, we
investigate how different versions of certain autoregressive language models --
GPT-2, GPT-3/3.5, Llama 2 and GPT-4 -- treat scope ambiguous sentences, and
compare this with human judgments. We introduce novel datasets that contain a
joint total of almost 1,000 unique scope-ambiguous sentences, containing
interactions between a range of semantic operators, and annotated for human
judgments. Using these datasets, we find evidence that several models (i) are
sensitive to the meaning ambiguity in these sentences, in a way that patterns
well with human judgments, and (ii) can successfully identify human-preferred
readings at a high level of accuracy (over 90% in some cases).