A quantum semantic framework for natural language processing (2506.10077v1)

Abstract: Semantic degeneracy represents a fundamental property of natural language that extends beyond simple polysemy to encompass the combinatorial explosion of potential interpretations that emerges as semantic expressions increase in complexity. LLMs and other modern NLP systems face inherent limitations precisely because they operate within natural language itself, making them subject to the same interpretive constraints imposed by semantic degeneracy. In this work, we argue using Kolmogorov complexity that as an expression's complexity grows, the likelihood of any interpreting agent (human or LLM-powered AI) recovering the single intended meaning vanishes. This computational intractability suggests the classical view that linguistic forms possess meaning in and of themselves is flawed. We alternatively posit that meaning is instead actualized through an observer-dependent interpretive act. To test this, we conducted a semantic Bell inequality test using diverse LLM agents as ``computational cognitive systems'' to interpret ambiguous word pairs under varied contextual settings. Across several independent experiments, we found average CHSH expectation values ranging from 1.2 to 2.8, with several runs yielding values (e.g., 2.3-2.4) that significantly violate the classical boundary ($|S|\leq2$). This demonstrates that linguistic interpretation under ambiguity can exhibit non-classical contextuality, consistent with results from human cognition experiments. These results inherently imply that classical frequentist-based analytical approaches for natural language are necessarily lossy. Instead, we propose that Bayesian-style repeated sampling approaches can provide more practically useful and appropriate characterizations of linguistic meaning in context.

• The paper proposes a quantum semantic framework for NLP, challenging classical views by treating meaning as observer-dependent and emergent from interactions, not intrinsic to text.
• It applies Kolmogorov complexity to demonstrate how semantic degeneracy makes precise interpretation computationally intractable as language complexity increases.
• Semantic Bell Tests with LLMs show violations of classical assumptions, suggesting non-classical contextuality in interpretation and advocating for non-classical, Bayesian-informed NLP approaches.

Analyzing a Quantum Semantic Framework for NLP

The paper "A quantum semantic framework for natural language processing" addresses the inherent challenges posed by semantic degeneracy within LLMs and other NLP systems. It argues that the complexity of semantic expressions results in computational intractability regarding precise interpretation, thereby undermining the classical notion that linguistic forms possess intrinsic meaning. Through the application of Kolmogorov complexity, the authors demonstrate how the probability of recovering a single intended meaning diminishes as an expression's complexity increases. This perspective implies that linguistic meaning is observer-dependent, requiring an interpretive act to actualize it.

Computational Insights: Semantic Degeneracy and Kolmogorov Complexity

The paper employs Kolmogorov complexity to frame semantic degeneracy as a fundamental property of natural language. Kolmogorov complexity provides a quantitative measure of the number of informational bits required to unambiguously specify the intended meaning of a semantic expression. As the complexity of expressions increases, the number of potential interpretations grows exponentially, decreasing the probability of perfect interpretation. This insight reveals why, despite their sophistication, LLMs struggle with deep, unambiguous understanding in complex and context-rich tasks. The combinatorial explosion of interpretations poses a significant barrier to the efficacy of DSMs and challenges the limits of classical probabilistic frameworks in NLP.

Quantum Semantics: A Novel Interpretive Framework

The paper introduces a quantum semantic framework as an alternative to classical approaches in NLP. This framework, reminiscent of quantum mechanics, considers semantic expressions as superpositions of potential interpretations, which are actualized through observer-dependent interactions. In this conceptualization, meaning emerges from the interaction between expression and observer rather than existing intrinsically within the text itself. By applying principles from quantum theory, including non-classical contextuality and measurement non-commutativity, the framework challenges traditional views of linguistic meaning. Such a conceptual shift aligns with broader cognitive theories emphasizing the dynamic and context-sensitive nature of meaning construction.

Experimental Methodology: Violations of Classical Locality

To substantiate their theoretical stance, the authors conduct a series of Semantic Bell Tests using diverse LLM agents as computational cognitive systems. These tests aim to identify non-classical correlations in semantic interpretation, mirroring CHSH-type Bell inequalities from quantum physics. Across several experiments, observing significant violations of classical CHSH bounds suggests that semantic interpretation within LLMs can exhibit non-classical contextuality. These findings resonate with similar experimental results in human cognition and decision-making, establishing non-classical contextuality as a pervasive feature of semantic processing in complex systems.

Implications for NLP and Future Directions

The paper's analysis underscores the need for methodological shifts in NLP towards non-classical, Bayesian-informed approaches. By embracing the probabilistic nature of semantic interpretation, future NLP systems may better characterize linguistic meaning through dynamic exploration and contextual sampling. Additionally, the concept of observer-dependent meaning actualization highlights the enduring importance of Human-in-the-Loop frameworks in critical applications. This perspective advocates for collaborative system architectures capable of leveraging contextual variability rather than attempting to eliminate it, thereby enhancing both robustness and resilience in language technologies.

Overall, the quantum semantic framework presents compelling implications for both theoretical understandings and practical applications in NLP. By illuminating the limitations of classical interpretation models and proposing a more adaptable and context-aware framework, the paper sets the stage for future advancements in computational linguistics and cognitive science.