Attribution and Citation: Sourcing LLM Outputs › Evaluating Attribution

Evaluating attribution requires determining whether a generated statement is supported by its cited source. This is itself a difficult natural language inference (NLI) problem: given a claim and a passage, does the passage support, contradict, or neither support nor contradict the claim?

The challenge is deeper than it might appear. Entailment is not a simple semantic similarity check. A passage that is topically related to a claim may not entail it. A passage that entails a claim from a different angle than the model's phrasing may be difficult for an NLI model to classify correctly. Negation, conditionals, and quantifiers all complicate entailment judgments in ways that challenge current NLI models.

Several formal evaluation frameworks and metrics have been developed for this task.

The Attributable to Identified Sources (AIS) framework defines a binary judgment for each statement in a model's output: is this statement fully attributable to the provided source material? A statement is AIS-compliant if a human, reading only the cited sources, could verify the statement as true.

This definition is deliberately strict. It requires that the verification be possible from the cited sources alone, without prior knowledge. A statement like "Albert Einstein was born in 1879" is common knowledge, but if the cited source does not contain this information, the statement fails the AIS check in the strict sense. This strictness is intentional: it forces systems to be explicit about what knowledge they are drawing on, rather than relying on implicit assumptions that the reader may or may not share.

AutoAIS uses a trained NLI model to automate this binary judgment. The AIS framework is notable because it is grounded in a precise definition of attribution: it is not enough that the statement is true, it must be verifiable from the cited sources. A true statement with no citation, or a citation that does not contain the supporting evidence, fails the AIS check.

The NLI model used for AutoAIS is typically a cross-encoder, meaning it takes both the passage and the claim as joint input and outputs a single entailment judgment. Cross-encoders are more accurate than bi-encoders for this task because they can model the interaction between premise and hypothesis directly, but they are also more computationally expensive since they cannot pre-compute passage representations independently.

The ALCE benchmark (Attributed LM Comprehension Evaluation) is an evaluation framework that tests whether language models can produce attributed responses across multiple tasks including open-domain question answering, slot filling, and document summarization.

ALCE measures two primary properties:

• Correctness: Does the response contain the right information, assessed against a gold-standard answer?
• Citation quality: Are the citations accurate, meaning does each cited passage support the accompanying claim?

ALCE uses a combination of automatic NLI-based evaluation and human annotation to assess citation quality. The framework's key insight is that correctness and attribution are independent properties: a model can be correct but uncited, incorrect but well-cited (citing a wrong source that happens to say something related), or correct and well-cited. The ideal system achieves both, but real systems often trade one for the other.

ALCE revealed a counter-intuitive pattern when first published: larger language models did not necessarily produce better attribution than smaller ones. A model might improve in factual accuracy as it scales, but if it was not specifically trained on attributed generation tasks, its citation quality could remain poor regardless of model size. This shows that attribution is a behavior that must be learned, not an automatic consequence of knowing more facts.

A more metric-oriented evaluation framework treats attribution evaluation like an information retrieval problem. For each claim in the generated text, we compute two complementary quantities.

Citation precision measures, among all the passage citations the model provided, what fraction support the claim. If $S$ is the set of cited passages and $S^+$ is the subset that supports the claim, then:

$$\text{Precision} = \frac{|S^+|}{|S|}$$

where:

• $S$: the set of all passages cited by the model for a given claim
• $S^+$: the subset of those cited passages that support the claim (as judged by NLI or human annotation)
• $|S^+|$ and $|S|$: the cardinalities of those sets

A precision of 1.0 means every citation the model provided supports its associated claim. A precision of 0.5 means half of the provided citations are irrelevant or incorrect. High precision is achieved when the model is selective: it only cites a passage when it is confident the passage supports the claim.

Citation recall measures, among all passages in the retrieved set that support the claim, what fraction are cited by the model. If $R^+$ is the set of all retrieved passages that support the claim, then:

$$\text{Recall} = \frac{|S^+|}{|R^+|}$$

where:

• $R^+$: all retrieved passages (regardless of whether they were cited) that support the claim
• $S^+$: the supporting passages that the model cited

A recall of 1.0 means the model cited every retrieved passage that was supportive. A recall of 0.5 means the model cited only half of the available supporting evidence, leaving relevant corroboration on the table.

These metrics can be computed at the sentence or claim level. They parallel standard IR precision/recall and share the same tradeoffs: systems can optimize for one at the cost of the other. A model that cites every retrieved passage will have high recall but low precision; a model that only cites when highly confident will have high precision but may miss supporting evidence.

The F1 score over citation precision and recall provides a balanced summary metric that equally weights both:

$$F_1 = \frac{2 \cdot \text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}}$$

This formula is undefined when both precision and recall are zero, in which case $F_1 = 0$ by convention.

The choice of whether to optimize precision, recall, or F1 depends on the application. In high-stakes settings where a false citation is actively dangerous, precision matters more. In research assistance settings where comprehensiveness is important, recall matters more. Most practical attribution evaluations report all three, allowing system designers to choose the operating point that fits their use case.

A different approach to attribution evaluation focuses on internal consistency rather than source matching. The idea is that a well-attributed claim should be reproducible: if you ask the model the same question multiple times with different retrieved contexts, a claim that is supported by evidence should appear consistently, while a hallucinated claim should be inconsistent across samples.

This approach, related to the consistency checking methods we discussed in the hallucination detection chapter, does not require a human to evaluate source documents. Instead, it relies on the model's own variance as a signal of reliability. Claims that appear in every sampled response are likely to be well-grounded; claims that appear only occasionally may be hallucinated.

The practical implementation uses sampling-based generation. Given a query and multiple different retrieved contexts (sampled from the same corpus), you generate multiple responses and look for claims that appear consistently across all of them. Consistent claims are attributed to the model's knowledge; inconsistent claims are flagged as potentially unreliable.

The limitation of consistency-based evaluation is that a consistently wrong model will score well, and a consistently correct model that appropriately varies its response based on context may score worse than it should. More fundamentally, this approach measures consistency, not truth. A model that has memorized an incorrect fact from training data and always repeats it will score perfectly on consistency metrics while being systematically wrong. Consistency-based evaluation is therefore most useful as a complement to source-based evaluation, not a replacement.