Diffusion Language Models as Open Quantum Systems
Diffusion language models generate text by reversing a classical diffusion process over token sequences.
ProofPaper ↗
Key points
- We show that their continuous-time forward and reverse dynamics can be embedded in the framework of open quantum systems, which describes quantum systems interacting with their environment.
- This connection provides a basis for extending diffusion sampling to superpositions of sentence states and for modifying the underlying generation dynamics.
- The same formulation yields Quantum-Inspired Remasking (QIR): perturbing the masked-diffusion dynamics produces a confidence-based rule for revising previously generated tokens.
- Experiments with pretrained diffusion language models show that superposition-based generation can retain information from conditioning components not selected at the final measurement, while QIR improves LLM-assessed text quality when applied at an appropriate stage of generation.
Sources (1)
- [1]Diffusion Language Models as Open Quantum SystemsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 10:57 AM
Diffusion language models generate text by reversing a classical diffusion process over token sequences.
We show that their continuous-time forward and reverse dynamics can be embedded in the framework of open quantum systems, which describes quantum systems interacting with their environment.
Extractive summary: sentences quoted from the sources.
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