DALM: Domain-Algebraic Language Model Introduces Three-Phase Structured Generation to Prevent Cross-Domain Contamination

A recent study has unveiled DALM (Domain-Algebraic Language Model), an innovative method for language modeling that tackles the challenge of knowledge interference among various domains in large language models. This model substitutes unrestricted token generation with structured denoising across a domain lattice, progressing through three distinct phases: first addressing domain uncertainty, then relation uncertainty, and finally concept uncertainty, all while adhering to explicit algebraic constraints. Key elements of the framework include a lattice of domains equipped with computable meet, join, and implication operations; a typing function managing inheritance across domains; and a fiber partition that confines knowledge to specific domain subsets. This setup results in a three-phase encoder-decoder system that restricts generation to particular domain fibers, effectively preventing cross-domain interference. The findings were shared on arXiv under the identifier 2604.15593v1, classified as a cross announcement. The paper contends that conventional large language models compress diverse knowledge into a single parameter space, leading to interference during generation. DALM's structured methodology seeks to alleviate this problem through algebraic constraints and domain-specific localization.