Attribution Fidelity

Measures the proportion of provided citations that correctly and relevantly support the specific claim they are attached to. A high precision score indicates that the system does not cite irrelevant or tangentially related sources to create a false appearance of authority.

• Penalizes hallucinated citations (references to non-existent sources)
• Penalizes gratuitous citations (correct source, but irrelevant to the claim)
• Calculated as: (Number of Correctly Supporting Citations) / (Total Number of Citations Provided)

Measures the proportion of all factual claims in a generated text that are correctly supported by an explicit citation to a verifiable source. High recall ensures the system is not making unsupported assertions.

• Identifies orphan claims (factual statements with no backing reference)
• Critical for high-stakes domains like medicine and law
• Calculated as: (Number of Cited Factual Claims) / (Total Number of Verifiable Factual Claims)

An NLI-based metric that determines whether the text of a cited source document logically entails the claim made in the generated output. This goes beyond keyword overlap to assess true semantic alignment.

• Uses a Natural Language Inference model fine-tuned for factual consistency
• Classifies each claim-source pair as entailment, contradiction, or neutral
• Detects misrepresentation, where a source is cited but its meaning is distorted

Evaluates the resolution at which citations are applied. High-fidelity systems provide fine-grained, claim-level attribution rather than a single blanket citation for an entire paragraph.

• Document-level: A single source cited for a block of text (low fidelity)
• Passage-level: A specific section or paragraph is referenced
• Claim-level: Each discrete factual assertion has its own pinpoint citation (high fidelity)
• Enables N-gram Provenance tracing for maximum transparency

Assesses whether the generated text preserves the original context and intent of the source material without cherry-picking, oversimplifying, or altering the author's intended meaning.

• Detects contextomy (quoting out of context to change meaning)
• Evaluates hedging preservation (whether scientific uncertainty is maintained)
• Checks for temporal context (ensuring outdated sources aren't presented as current fact)

Verifies that the complete provenance trail from the original source to the final citation remains intact and auditable. A break in the chain indicates a potential fidelity failure.

• Validates transitive attribution across summarization steps
• Detects attribution drift where meaning degrades with each hop
• Requires cryptographic provenance or transparency logs for full auditability
• Critical for systems using multi-hop retrieval or recursive summarization

The two foundational metrics for quantifying attribution fidelity. Citation Precision measures the proportion of provided citations that correctly support their attached claim (avoiding irrelevant or hallucinated references). Citation Recall measures the proportion of factual claims in a generated text that are backed by a verifiable citation.

• A system with high recall but low precision cites everything but cites poorly
• A system with high precision but low recall cites perfectly but leaves most claims unsupported
• The F1 score harmonizes both into a single fidelity metric

The progressive degradation of citation accuracy as information passes through successive layers of summarization or generation. An original source may state a specific finding, but a summary of a summary may distort that finding while still citing the original paper.

• Common in multi-hop RAG pipelines where retrieved passages are summarized before final generation
• Mitigated by N-gram Provenance techniques that trace specific text sequences back to their exact origin in the source document
• Represents a silent failure mode where citations appear valid but are semantically misaligned

An architectural pattern where a language model explicitly cites the specific passages from retrieved documents used to generate a response. Unlike generic document-level citations, this approach enables passage-level verification.

• The model outputs both the claim and a pointer to the exact text span that grounds it
• Enables automated verification by comparing the generated claim against the cited passage using Natural Language Inference (NLI) models
• Forms the technical basis for high-fidelity attribution in enterprise RAG systems

Algorithmic evaluation frameworks that assess the quality, relevance, and trustworthiness of a source cited by an AI. These scores go beyond binary correctness to evaluate:

• Source Authority: The historical reliability and expertise of the cited domain or author
• Semantic Alignment: The degree to which the cited document actually contains the claimed information
• Temporal Relevance: Whether the source is current enough to support the claim
• Composite scores feed into broader Trust Scoring Algorithms that inform downstream ranking and filtering decisions

The predictive methodologies used to estimate the likelihood that a generated statement contains factual errors before human review. These assessments are critical for calibrating attribution fidelity expectations.

• Internal Confidence Scores: The model's own token-level probability estimates
• Semantic Entropy: Measuring the consistency of meaning across multiple sampled generations for the same prompt
• Retrieval Score Thresholding: Flagging outputs where the retrieval confidence falls below a calibrated threshold
• High-risk outputs can be routed to Fact-Checking Automation pipelines for verification

The process of aligning a model's predicted probability of correctness with its actual empirical accuracy. A well-calibrated model that claims 90% confidence should be correct exactly 90% of the time.

• Critical for attribution fidelity because miscalibrated confidence leads to overconfident false citations
• Measured using Expected Calibration Error (ECE) and reliability diagrams
• Post-hoc calibration techniques include Platt scaling and isotonic regression applied to the model's output logits