TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing, Data Science & Analytics · Depth: Expert, extended

Summary

SPAD (Seven-Source Token Probability Attribution with Syntactic Aggregation) is a novel framework designed to detect hallucinations in Retrieval-Augmented Generation (RAG) systems by providing a comprehensive, mechanistic view of token generation. Unlike prior methods that focus on a binary conflict between internal FFN knowledge and retrieved context, SPAD mathematically attributes each token's probability to seven distinct sources: Query, RAG, Past, Current Token, FFN, Final LayerNorm, and Initial Embedding. These attribution scores are then aggregated by Part-of-Speech (POS) tags to identify anomalous linguistic patterns, such as Nouns relying heavily on Final LayerNorm, which signal hallucinations. The framework, which uses an XGBoost classifier on a 126-dimensional feature vector, has demonstrated state-of-the-art performance on benchmarks like RAGTruth and Dolly, outperforming existing baselines across Llama2-7B, Llama2-13B, and Llama3-8B models. SPAD also offers transparent interpretability, revealing that hallucination signals vary across model architectures and highlighting the often-overlooked role of the user query.

Key takeaway

For AI Engineers and Research Scientists developing or deploying RAG systems, SPAD offers a more robust and interpretable approach to hallucination detection. By analyzing the seven distinct sources of token probability and their syntactic context, you can move beyond proxy signals to pinpoint the architectural causes of factual errors. This enables more precise debugging and the potential for real-time mitigation strategies, improving the reliability of your LLM applications.

Key insights

SPAD attributes token probabilities to seven sources and aggregates by POS tags to detect RAG hallucinations.

Principles

• Hallucination detection requires comprehensive source attribution.
• Syntactic context is crucial for interpreting attribution scores.
• Hallucination signals are model-specific.

Method

SPAD decomposes token probabilities into seven sources, then aggregates these attributions by Part-of-Speech tags to create a feature vector for an XGBoost classifier, identifying anomalies indicative of hallucinations.

In practice

• Monitor LayerNorm contributions for numerical reasoning.
• Analyze Query attribution for prompt-driven hallucinations.
• Use POS-aware attribution for nuanced detection.

Topics

• RAG Hallucination Detection
• Token Probability Attribution
• Syntactic Aggregation
• Transformer Architecture
• Llama Models

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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• Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems — Full-context verification for RAG systems significantly improves hallucination detection in long documents under real-time constraints.
• Hallucinations in LLMs: A Deep Technical Dive into Causes, Detection, and Mitigation — LLM hallucinations are inherent to their probabilistic nature, driven by training objectives that prioritize fluency over factual accuracy.
• Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection — Evidence Graph Consistency (EGC) uses structural relationships to detect RAG hallucinations, but its diagnostic direction varies significantly across LLM families.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.