# A Quantum-Inspired Analysis of Human Disambiguation Processes

arXiv: https://arxiv.org/abs/2408.07402 · HTML: https://mlsanity.com/paper/2408.07402/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 204 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Causal models in string diagrams](https://mlsanity.com/paper/2304.07638/) (2023)
- [An Introduction to String Diagrams for Computer Scientists](https://mlsanity.com/paper/2305.08768/) (2023)
- [On the Quantum-like Contextuality of Ambiguous Phrases](https://mlsanity.com/paper/2107.14589/) (2021)

## Abstract (arXiv)

Formal languages are essential for computer programming and are constructed to be easily processed by computers. In contrast, natural languages are much more challenging and instigated the field of Natural Language Processing (NLP). One major obstacle is the ubiquity of ambiguities. Recent advances in NLP have led to the development of large language models, which can resolve ambiguities with high accuracy. At the same time, quantum computers have gained much attention in recent years as they can solve some computational problems faster than classical computers. This new computing paradigm has reached the fields of machine learning and NLP, where hybrid classical-quantum learning algorithms have emerged. However, more research is needed to identify which NLP tasks could benefit from a genuine quantum advantage. In this thesis, we applied formalisms arising from foundational quantum mechanics, such as contextuality and causality, to study ambiguities arising from linguistics. By doing so, we also reproduced psycholinguistic results relating to the human disambiguation process. These results were subsequently used to predict human behaviour and outperformed current NLP methods.