Quantum-Like Contextuality in Large Language Models

TL;DR

This study demonstrates large-scale evidence of quantum-like contextuality in natural language using BERT, suggesting quantum methods could enhance language processing tasks.

Contribution

First large-scale experimental demonstration of quantum-like contextuality in natural language, linking it to semantic similarity and embedding distances.

Findings

Identified over 77,000 sheaf-contextual instances

Discovered nearly 37 million CbD contextual instances

Established a relationship between contextuality and BERT embedding distances

Abstract

Contextuality is a distinguishing feature of quantum mechanics and there is growing evidence that it is a necessary condition for quantum advantage. In order to make use of it, researchers have been asking whether similar phenomena arise in other domains. The answer has been yes, e.g. in behavioural sciences. However, one has to move to frameworks that take some degree of signalling into account. Two such frameworks exist: (1) a signalling-corrected sheaf theoretic model, and (2) the Contextuality-by-Default (CbD) framework. This paper provides the first large scale experimental evidence for a yes answer in natural language. We construct a linguistic schema modelled over a contextual quantum scenario, instantiate it in the Simple English Wikipedia and extract probability distributions for the instances using the large language model BERT. This led to the discovery of 77,118 sheaf-contextual and 36,938,948 CbD contextual instances. We proved that the contextual instances came from semantically similar words, by deriving an equation between degrees of contextuality and Euclidean distances of BERT's embedding vectors. A regression model further reveals that Euclidean distance is indeed the best statistical predictor of contextuality. Our linguistic schema is a variant of the co-reference resolution challenge. These results are an indication that quantum methods may be advantageous in language tasks.