Enterprise AI Analysis
SPAD: Detecting Hallucinations in RAG with Seven-Source Token Probability Attribution and Syntactic Aggregation
A novel framework that mathematically attributes token probabilities to seven distinct sources, aggregates by POS tags, and achieves state-of-the-art hallucination detection in RAG systems. This deep dive uncovers the mechanistic logic driving reliable AI outputs.
Quantifiable Impact for Your Business
SPAD's advanced attribution and detection capabilities translate directly into higher reliability and control over your RAG-powered applications.
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F1-score (LLaMA2-13B RAGTruth)
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AUC (LLaMA2-13B RAGTruth)
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F1-score (LLaMA3-8B RAGTruth)
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Information Sources Tracked
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Experimental Results
Interpretability
Enterprise Process Flow
Coarse-Grained Decomposition
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Fine-Grained Attribution & Source Mapping
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Syntax-Aware Feature Engineering
Core Innovation
7 Distinct Attribution Sources for Token Probability| SPAD (Our Approach) | Traditional Proxy Signals |
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LLaMA2-13B RAGTruth Performance
0.7912 Achieved F1-scoreLLaMA3-8B RAGTruth Performance
0.7975 Achieved F1-scoreRobustness in Data-Scarce Environments
Top F1-score On Dolly (AC) dataset with extreme data scarcityKey Insights into Hallucination Drivers
SPAD's interpretability analysis reveals three key findings: 1. The syntax of grounding varies by architecture. Llama2 models (7B/13B) rely primarily on content words (RAG_NOUN), while Llama3-8B relies on relational structures (RAG_ADP). 2. LayerNorm on Numerals is a critical but flip-flopping signal. In Llama2-7B, high LN_NUM attribution acts as a warning sign for hallucination, but in Llama2-13B, it indicates factuality, demonstrating model-specific behaviors. 3. The User Query is an overlooked but critical hallucination driver. Query-based features frequently rank among the top predictors (e.g., QUERY_ADJ and QUERY_NOUN), challenging the traditional focus on just RAG and FFNs and highlighting the prompt's vital role.
Calculate Your Potential ROI
Understand the tangible benefits of integrating advanced hallucination detection into your enterprise AI pipeline. Estimate annual savings and reclaimed human hours.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Journey to Trustworthy AI
Implementing SPAD into your enterprise AI ecosystem is a structured process designed for seamless integration and maximum impact.
Phase 1: Discovery & Customization
We begin with a deep dive into your existing RAG architecture and specific hallucination challenges. This phase involves fine-tuning SPAD's detection mechanisms to align with your unique data, models, and compliance requirements.
Phase 2: Integration & Pilot Deployment
SPAD is integrated into your development and testing pipelines. We conduct a pilot deployment on a subset of your RAG applications, closely monitoring performance and gathering feedback to optimize for your production environment.
Phase 3: Scaled Rollout & Continuous Monitoring
Following a successful pilot, SPAD is deployed across your entire RAG infrastructure. Our team provides ongoing support, updates, and advanced analytics to ensure sustained performance and adaptation to evolving AI models.
Ready to Enhance Your AI Trustworthiness?
Don't let hallucinations compromise your AI initiatives. Partner with us to implement SPAD and achieve unparalleled control and reliability.
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