Attribution and Citation: Sourcing LLM Outputs › Training Models for Attribution

Producing reliable attribution requires more than inference-time changes; it requires models that have been trained to attribute their claims. This training dimension is often underappreciated in discussions of attribution evaluation, which focus on how to measure attribution quality without asking how to produce it in the first place.

The primary approach is supervised fine-tuning on attributed corpora: large collections of (query, retrieved passages, attributed response) triples where the response contains inline citations that have been verified as correct. These training examples teach the model the behavioral pattern of citing passages as it generates text, rather than generating freely and adding citations as an afterthought.

Collecting high-quality attributed training data is expensive. Human annotators must read passages and generated responses, identify which passage supports which claim, and verify that the attributions are accurate. This annotation process requires careful instruction because the judgments are difficult: what counts as sufficient support for a claim? When is a paraphrase close enough to the original passage to count as attributed? When does the model's synthesis go beyond what any single passage supports?

One approach to reducing annotation costs is to use existing attributed text as supervision. Academic papers with in-text citations are a natural source of attributed text: each sentence with a citation is an example of a claim linked to a source. Preprocessing these papers, extracting the cited passages, and treating each in-text citation as a training example creates a large supervised dataset at relatively low cost. The challenge is that academic citation conventions differ from the in-context RAG attribution we typically want: academic citations point to external papers rather than to retrieved passages in the model's context, and academic authors sometimes cite papers for background rather than for specific factual support.

Reinforcement learning approaches can also be used to train for attribution. The reward signal is attribution accuracy: given a response with citations and the corresponding retrieved passages, an NLI-based judge assigns a reward based on how well each citation is supported. The model is trained to maximize this reward using policy gradient methods. This approach sidesteps the need for human annotation of training examples, since the NLI judge provides the training signal automatically. The limitation is that the NLI judge's errors become the model's training signal, so systematic NLI errors will be reinforced rather than corrected.

A third approach is constitutional AI methods applied to attribution: the model generates responses, critiques its own citation quality using explicit attribution criteria ("does this citation actually support this claim?"), and revises its response to improve attribution. This iterative self-critique can improve citation quality without requiring external supervision, though it depends on the model's ability to accurately assess its own attribution accuracy, which is itself a form of the evaluation problem we are trying to solve.