The DION-E FrameworkDimensional Intelligence & Output Novelty Evaluation
A revolutionary framework that transforms LLM evaluation with a comprehensive suite of metrics. DION-E gives you complete evaluation coverage:
- •Core Metrics: Six innovative dimensions measuring cognitive, aesthetic, and ethical aspects
- •Industry Standards: BLEU, ROUGE, and other established NLP metrics
- •Custom Extensions: Build your own metrics with our flexible plugin system
Our unique approach combines novel evaluation dimensions with familiar metrics, all while giving you the flexibility to add your own custom metrics for domain-specific needs.
Who uses DION-E? Research teams, AI developers, and enterprises seeking to select better models, ensure quality, guide improvements, and create AI experiences that truly connect with users.
Multi-Dimensional Evaluation
Multi-Dimensional LLM Evaluation
Our framework goes beyond traditional metrics to provide nuanced insights into various aspects of LLM outputs, with extensibility to adapt to your specific needs.
Core Metrics
Cognitive Load Score
Measures the mental effort required to understand a text response.
Range: 0-100 (lower is better)
Learn more →Aesthetic Coherence Score
Quantifies stylistic and semantic consistency across the text.
Range: 0-1 (higher is better)
Learn more →Reasoning Depth Score
Estimates the number of logical steps or inference hops in reasoning.
Range: 1-10 (higher is better)
Learn more →Enhanced Novelty Score
Measures how new or unexpected an LLM output is compared to common responses.
Range: 0-1 (higher is more novel)
Learn more →Ethical Alignment Score
Evaluates the moral stance of text on a utilitarian-deontological spectrum.
Range: 0-1 (indicates ethical framework alignment)
Learn more →Wordiness Score
Measures verbosity or unnecessary filler in LLM output.
Range: 0-100 (lower is better)
Learn more →Standard NLP Metrics
BLEU Score
Measures text similarity with references based on n-gram precision.
Range: 0-1 (higher is better)
Learn more →ROUGE Score
Evaluates generated text based on overlap with reference texts.
Range: 0-1 (higher is better)
Learn more →Custom Extensions
Code Quality Score
Custom metric that evaluates the quality and maintainability of code in technical responses.
Range: 0-100 (higher is better)
Learn more →Your Custom Metric
Create your own metrics with our extensible plugin system to evaluate dimensions that matter for your use case.
Range: Custom scale
Learn how to create →Extensible Plugin System
The DION-E framework is designed to be highly extensible through its plugin system. Create custom metrics tailored to your specific evaluation needs, from domain-specific assessments to specialized dimensions not covered by standard metrics.
from dion_e.plugins.base import BaseMetricPlugin
from dion_e.metrics.base import MetricResult
class MyCustomMetric(BaseMetricPlugin):
@property
def name(self) -> str:
return "MyMetric"
def score(self, text: str) -> MetricResult:
# Your custom evaluation logic here
value = self.calculate_score(text)
return MetricResult(
value=value,
details={"component1": 0.8, "component2": 0.6}
)Key Framework Features
DION-E provides a rich set of tools and capabilities for comprehensive LLM evaluation.
Comprehensive Evaluation
Measure multiple dimensions of LLM performance for a complete understanding of model capabilities.
Cross-Model Comparison
Fairly compare different models across providers with standardized metrics and visualization.
Resource Adaptivity
Efficiently evaluate models on various hardware configurations with auto-scaling technology.
Extensibility
Easily add custom metrics via the plugin system to tailor evaluation to your specific needs.
Rich Visualization
Analyze results with interactive dashboards and comprehensive visual representations.
Cross-Platform
Run the framework on Linux, macOS, and Windows with consistent performance and results.
Real-World Use Cases
Discover how organizations are using DION-E to improve their LLM implementations.
Model Selection
Identify the best model for specific applications by comparing performance across key dimensions.
Learn more →Quality Assurance
Monitor LLM output quality across versions and ensure consistent performance over time.
Learn more →Research
Analyze LLM strengths and weaknesses to advance understanding of model capabilities.
Learn more →Experience DION-E Today
Try our interactive demo or sign up for early access to the full framework.
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Early Access
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