NRR-Phi: Text-to-State Mapping for Ambiguity Preservation in LLM Inference
Summary: arXiv:2601.19933v5 Announce Type: replace-cross
Abstract: Large language models exhibit a systematic tendency toward early semantic commitment: given ambiguous input, they collapse multiple valid interpretations into a single response before sufficient context is available. This premature collapse discards information that may prove essential as dialogue evolves. We present a formal framework for text-to-state mapping (phi: T -> S) that transforms natural language into a non-collapsing state space where multiple interpretations coexist.
Framework Overview
The mapping decomposes into three stages:
- Conflict Detection: Identifying ambiguous elements within the input text.
- Interpretation Extraction: Enumerating possible meanings and interpretations from the detected conflicts.
- State Construction: Creating a state representation that accommodates multiple interpretations without collapsing them.
Implementation Details
We instantiate φ with a hybrid extraction pipeline that combines rule-based segmentation for explicit conflict markers with LLM-based enumeration of implicit ambiguity. On a test set of 68 ambiguous sentences, the resulting states preserve interpretive multiplicity. The hybrid extraction yields a mean state entropy (H) of 1.087 bits across various ambiguity categories, significantly higher than H = 0 observed in collapse-based baselines that commit to a single interpretation.
Cross-Lingual Portability
Our framework also illustrates cross-lingual portability by implementing the rule-based conflict detector for Japanese markers. This adaptation showcases the framework’s flexibility and potential for various languages, enabling broader applications in multilingual settings.
Extending Non-Resolution Reasoning (NRR)
This framework extends Non-Resolution Reasoning (NRR) by providing the algorithmic bridge between text and the NRR state space, enabling architectural collapse deferment in LLM inference. The design principles for state-to-state transformations are detailed in the Appendix, offering a comprehensive guide for implementation.
Empirical Validation
Empirical validation conducted on 580 test cases demonstrates a remarkable performance. The results indicate a 0% collapse for principle-satisfying operators, in stark contrast to the 17.8% collapse found for operators that violate the principles. This finding underscores the effectiveness of our approach in maintaining interpretive richness in LLM outputs.
Conclusion
In conclusion, the NRR-Phi framework presents a significant advancement in the field of natural language processing by addressing the challenges associated with ambiguity in large language models. By preserving interpretive multiplicity through a structured approach to text-to-state mapping, this framework not only enhances dialogue quality but also opens avenues for further research in multi-interpretation scenarios across languages.