Authors respond to critiques on quantum features in human and AI language

Reply to comments arXiv:2512.07881 and arXiv:2601.06104 on quantum structure in human and AI-generated language

Computation and Language

Summary

This paper is a reply to comments about earlier studies that explored quantum-like statistical patterns in human and AI-generated language. The authors clarify their experimental methods with language models and explain why certain quantum concepts, like entanglement and contextuality, are relevant. They also address misunderstandings about their data analysis and correct a minor error that doesn't affect their key findings. Overall, they defend their approach to studying language through ideas borrowed from quantum physics.

What this means in practice

A position paper. It proposes an approach and reports no results.

Authors

Massimiliano Sassoli de Bianchi, Roberto Leporini

Abstract

We reply to the comments by M. Sienicki and K. Sienicki (arXiv:2512.07881) and by K. Sienicki (arXiv:2601.06104) on our work on quantum-mechanical statistics in human language (arXiv:2407.14924) and on quantum structure in AI-generated language (arXiv:2511.21731). We thank the authors for their careful reading and address what we consider to be the main points of criticism: the exploratory nature of the protocol used in the experiments with large language models; the role of marginal-law violations, and of the Contextuality-by-Default criterion, in the identification of entanglement; the limited diagnostic value of a Bose-Einstein fit taken in isolation; the meaning of assigning the lowest energy levels to the most frequent words; and the relation between the vector spaces used by LLMs and quantum state spaces. We also correct a typographical error in Table 3 of arXiv:2511.21731, which does not affect the reported CHSH value.