mlsanityData to 7 Oct 2026

The production of meaning in the processing of natural language

arXiv 2603.20381 ↗First posted 20 Mar 2026cs.CL cs.AI cs.HCcited by 1

Not on the map. Why?

Abstract (arXiv)

Understanding the fundamental mechanisms governing the production of meaning in the processing of natural language is critical for designing safe, thoughtful, engaging, and empowering human-agent interactions. If meaning is constituted rather than retrieved, then the search for context-independent features or circuits in the pursuit of mechanistic interpretability may be fundamentally limited. Experiments in cognitive science and social psychology have demonstrated that human semantic processing exhibits contextuality more consistent with quantum logical mechanisms than classical Boolean theories, and recent works have found similar results in large language models---in particular, clear violations of the Bell inequality in experiments of contextuality during interpretation of ambiguous expressions. In this work, we explore the CHSH |S| parameter---the metric associated with the inequality---across the inference parameter space of models spanning four orders of magnitude in scale and cross-reference our findings with MMLU, hallucination rate, and nonsense detection benchmarks. We find that the interquartile range of the |S| distribution is completely orthogonal to all external benchmarks, while overall violation rate shows weak anticorrelation with all three benchmarks. We investigate how |S| varies with sampling parameters and word order, and discuss the information-theoretic constraints that genuine contextuality imposes on prompt injection defenses and its human analogue, whereby careful construction and maintenance of social contextuality can be carried out at scale, shaping the space of possible interpretations before any particular one is reached. We consider the implications for mechanistic interpretability and how genuine contextuality sets an information-theoretic bound on the decomposability of semantic processing.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 23 of its 72 references that are in the map. First posted on arXiv 20 Mar 2026.

Builds on

23 of 72 references.

References · 23

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  3. 2022Emergent Abilities of Large Language Modelscited by 2,483
  4. 2023Sparse Autoencoders Find Highly Interpretable Features in Language Modelscited by 1,359
  5. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
  6. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  7. 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 515
  8. 2022Using cognitive psychology to understand GPT-3cited by 389
  9. 2024How to use and interpret activation patchingcited by 191
  10. 2024The Effect of Sampling Temperature on Problem Solving in Large Language Modelscited by 168
  11. 2023The Quantization Model of Neural Scalingcited by 137
  12. 2022Locally Typical Samplingcited by 108
11 more
  1. 2024Understanding Emergent Abilities of Language Models from the Loss Perspectivecited by 94
  2. 2024Is Temperature the Creativity Parameter of Large Language Models?cited by 92
  3. 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
  4. 2023Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patchingcited by 75
  5. 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systemscited by 59
  6. 2025Exploring the Impact of Temperature on Large Language Models:Hot or Cold?cited by 40
  7. 2023On the Efficacy of Sampling Adapterscited by 17
  8. 2017Spin and Wind Directions II: A Bell State Quantum Modelcited by 5
  9. 2025A quantum semantic framework for natural language processingcited by 5
  10. 2015Spin and Wind Directions I: Identifying Entanglement in Nature and Cognitioncited by 4
  11. 2024Quantum-Like Contextuality in Large Language Modelscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.