Scope Ambiguities in Large Language Models

arXiv:2404.04332v1 Announce Type: new
Abstract: 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 …

abstract arxiv cs.ai cs.cl insights interpretation knowledge language language models language processing large language large language models modern multiple operators processing research semantic them type world