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Inside the Black Box: Interpreting LLMs with DLBacktrace (DLB)
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ML Explainability
Jun 5

Inside the Black Box: Interpreting LLMs with DLBacktrace (DLB)

Explore how DLBacktrace can empower your leadership with transparent, accountable, and compliant AI strategies

Is Explainability critical for your AI solutions?

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AryaXAI provides the most accurate explainability and alignment stack to deliver accurate, true-to-model explainability, monitoring, risk management, and alignment techniques essential for highly mission-critical or regulated AI solutions.

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Privacy Evaluation
F-Score (F1-Score)
Constant Features
High Feature Correlation
Target Drift
Stochastic Gradient Descent (SGD)
RandomForest
CatBoost (Categorical Boosting)
LightGBM (Light Gradient Boosting Machine)
XGBoost (eXtreme Gradient Boosting)
CTGAN (Conditional Tabular Generative Adversarial Network)
GPT-2 (Generative Pre-trained Transformer 2)
Internet Information Service Algorithm Recommendation Management Regulations
Generative AI Measures in China
Provisions on the Administration of Deep Synthesis of Internet-based Information Services
Artificial Intelligence and Algorithmic Fairness Initiative
The EU AI Act
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Federal Trade Commission (FTC)
President Biden's Executive Order on AI
Principles for Responsible AI
Digital India Act
Draft National Data Governance Framework Policy
National Strategy for Artificial Intelligence #AIFORALL: NITI Aayog
National Cybersecurity Reference Framework
Global Partnership on Artificial Intelligence (GPAI)
Top-k
Temperature
Low-Rank Adaptation (LoRA)
Quantization
Hallucination
Multi-modal models
Mixture of experts (MoEs)
Mamba
Opensource vs. Closed Source Models
Tokens
Diffusion Models
Transformers Models
Model safety
Vector database
Large Language Models (LLMs)
Foundation models
Synthetic AI
Generative AI
Z-Test
Chi-square test
Kolmogorov–Smirnov test (K–S test or KS test)
Wasserstein distance
Jensen-Shannon(JS) Divergence
Population Stability Index (PSI)
Kullback-Leibler (KL) divergence
ML Audit
Bias Monitoring
Data Monitoring
ML Observability
MLOps
Regression model
Classification model
Model confidence score
Model threshold
Surrogate models
What-if
Similar Cases
Feature Importance Store
LOCO
SHAP
LIME
Explainability
Fairness/ Bias Monitoring
ML Monitoring
Accuracy
Data Processing
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Recall/ Sensitivity or True Positive Rate
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Precision-recall curve
Confusion Matrix
F score
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Data Drift
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