Level of interaction (e.g., triadic for "crocodile tears eyes"),
Pattern orientation (e.g., metaphorical dominance),
Implicit weight (based on cultural salience lexicon),
Resonance score (contextual stability index across corpora),
Probabilistic fit (likelihood derived from idiomatic database or annotated corpora),
Projected output type (denotative, idiomatic, artistic).
2. Training the Semantic Layer
The CAS-6 semantic layer is trained using a multi-objective loss function jointly with the base model. This function optimizes for both:
Traditional language modeling loss LLM\mathcal{L}_{LM}, and
Semantic coherence loss LCAS6\mathcal{L}_{CAS6}, which measures:
Alignment of predicted output types with gold semantic annotations,
Deviation from stable interaction graphs in canonical phrases,
Attenuation or amplification of implicit weight across plausible vs. implausible expressions.
Ltotal=1LLM+2LCAS6\mathcal{L}_{total} = \lambda_1 \mathcal{L}_{LM} + \lambda_2 \mathcal{L}_{CAS6}
Where 1,2\lambda_1, \lambda_2 are tunable weights.
Supervision for the CAS-6 layer can be derived from:
Idiom and metaphor corpora (e.g., VU Amsterdam Metaphor Corpus),
Cultural lexicons and semiotic datasets,
Crowdsourced semantic resonance evaluations,
Annotated narrative and poetic texts with implicit-meaning metadata.
3. Expected Gains from the CAS-6 Semantic Layer
By incorporating a graph-theoretical and dynamic-interactional approach to semantics, the CAS-6 semantic layer allows:
Interpretation of non-literal expressions (e.g., "crocodile tears" insincere emotion),
Filtering of semantically unstable combinations, even if statistically probable,
Cultural adaptation via dynamic re-weighting of interaction weight vectors per context or domain,
Explainability through visualizable interaction graphs that can be traced and interpreted by researchers or end-users.
4. Modularity and Compatibility
The CAS-6 semantic layer is model-agnostic and can be incorporated into various pre-trained architectures (e.g., GPT, T5, LLaMA) during the fine-tuning phase. Because it relies on inter-token interaction dynamics rather than raw embeddings, the layer:
Does not alter core transformer mechanics,
Can be pre-trained separately using auxiliary datasets,
Allows for post-hoc reasoning modules, enhancing interpretability and alignment.
Conclusion of Section 5.A
The integration of a CAS-6-based semantic layer introduces an adaptive, resonance-sensitive dimension to LLM fine-tuning. This framework empowers LLMs not merely to predict syntactically coherent outputs but to interpret and generate language with aesthetic, metaphorical, and cultural depth. It sets the stage for a new generation of LLMs capable of truly understanding---not merely emulating---the richness of human language.
