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A zero-dependency Python standard library skill for estimating LLM hallucination and confabulation via semantic entropy (Kuhn et al.). Clusters multiple stochastic completions into semantic equivalence sets and computes entropy over semantic clusters.
graph TD
A[Stochastic LLM Samples] --> B[N-gram Semantic Equivalence Grouping]
B --> C[Cluster Assignment]
C --> D[Compute Cluster Probability P_C]
D --> E[Semantic Entropy -sum P_C log P_C]
E --> F{Entropy >= Threshold?}
F -->|Yes| G[Flag Hallucination / Confabulation]
F -->|No| H[Verified Factual Alignment]Related MCP server: groundlens-deprecated
Semantic Equivalence Clustering: Group responses by semantic meaning rather than exact token matches.
Pure Python 3.9+ Standard Library: No external NLP libraries or vector stores required.
Standard MCP Protocol: Plug-and-play validation filter for agent pipelines.
Platform: GenPark AI
MCP Registry: GenPark MCP Hub
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Scores text for substance, depth, and clarity in under 25ms.
LLM & RAG evals: hallucination, eval contracts, semantic tests, tokens & model info. Free, no key.
Explainable ambiguity signals for text. Submitted text is processed but never retained.
Checks if a cheaper model agrees with your GPT, Claude or Gemini outputs, and what routing saves.
