Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis
arXiv Preprint Archive June 26, 2025 Peer reviewed via arXiv
Summary
Applying Integrated Information Theory (IIT) 3.0 and 4.0 to sequences of large language model (LLM) representations from Theory of Mind tests reveals no statistically significant indicators of consciousness phenomena. The study compared IIT metrics—Φ^max, Φ, Conceptual Information, and Φ-structure—with Span Representations independent of consciousness estimates. Results show that contemporary Transformer-based LLM representations lack significant consciousness indicators, though spatio-permutational analyses revealed intriguing patterns.
Study at a glance
| Design | systematic analysis |
|---|---|
| Population | sequences of Large Language Model (LLM) representations from Theory of Mind test results |
| Key finding | Sequences of contemporary Transformer-based LLM representations lack statistically significant indicators of observed consciousness phenomena. |
Abstract
Integrated Information Theory (IIT) provides a quantitative framework for explaining consciousness phenomenon, positing that conscious systems comprise elements integrated through causal properties. We apply IIT 3.0 and 4.0 -- the latest iterations of this framework -- to sequences of Large Language Model (LLM) representations, analyzing data derived from existing Theory of Mind (ToM) test results. Our study systematically investigates whether the differences of ToM test performances, when presented in the LLM representations, can be revealed by IIT estimates, i.e., $\Phi^{\max}$ (IIT 3.0), $\Phi$ (IIT 4.0), Conceptual Information (IIT 3.0), and $\Phi$-structure (IIT 4.0). Furthermore, we compare these metrics with the Span Representations independent of any estimate for consciousness. This additional effort aims to differentiate between potential "consciousness" phenomena and inherent separations within LLM representational space. We conduct comprehensive experiments examining variations across LLM transformer layers and linguistic spans from stimuli. Our results suggest that sequences of contemporary Transformer-based LLM representations lack statistically significant indicators of observed "consciousness" phenomena but exhibit intriguing patterns under $\textit{spatio}$-permutational analyses. The Appendix and code are available as Supplementary Materials at: https://doi.org/10.1016/j.nlp.2025.100163.