Algorithmic Challenge: How do we mathematically audit semantic authority in LLMs? (Open-sourcing LSW)
The article introduces a theoretical framework called the LSW Index to mathematically audit semantic authority in large language models (LLMs). It proposes a multi-factor vector equation—LSW = (0.4α + 0.3β + 0.3γ) - Noise—to evaluate brand presence in latent semantic spaces. The framework is open-sourced with a Python implementation using mock embeddings, inviting developer feedback on its robustness and optimization.
- ▪The LSW Index is a proposed metric to audit semantic authority in LLMs using a weighted combination of semantic anchoring, sentiment stability, and relational proximity.
- ▪Semantic Anchoring (α) measures category-entity probability, Sentiment Stability (β) evaluates polarity variance, and Relational Proximity (γ) assesses cosine similarity to authoritative industry terms.
- ▪The Python implementation includes mock embedding functions and was tested using NVIDIA as a baseline case study.
- ▪The LSW formula normalizes scores to a 0–100 scale and subtracts a noise factor to compute a final index value.
- ▪The framework is open-sourced and presented as a tool for developers to assess embedding stability and semantic drift in enterprise LLM applications.
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