X-Blocks introduces a hierarchical framework analyzing natural language explanations for automated vehicles (AVs) at context, syntax, and lexicon levels. RACE, a multi-LLM ensemble with Chain-of-Thought and self-consistency, achieves 91.45% accuracy on Berkeley DeepDrive-X dataset. It uncovers scenario-specific vocabulary patterns and reusable grammar families for explainable AI.
Key Points
- 1.Hierarchical X-Blocks framework analyzes explanations at context-syntax-lexicon levels
- 2.RACE multi-LLM classifier hits 91.45% accuracy and 0.91 Cohen's kappa on DeepDrive-X
- 3.Log-odds with Dirichlet priors reveals scenario-specific vocabulary patterns
- 4.Dependency parsing extracts reusable grammar templates varying by predicate and causal types
Impact Analysis
Provides evidence-based principles for scenario-aware NLG in AVs, boosting trust and transparency. Dataset-agnostic design extends to other safety-critical AI domains like robotics.
Technical Details
RACE combines CoT reasoning and self-consistency across LLMs for robust context classification into 32 categories. Lexical analysis uses informative priors; syntax employs dependency parsing for template extraction.