Evaluating Evidence Attribution in Generated Fact Checking Explanations (2025)

Abstract: Automated fact-checking systems often struggle with trustworthiness, as their generated explanations can include hallucinations. In this work, we explore evidence attribution for fact-checking explanation generation. We introduce a novel evaluation protocol, citation masking and recovery, to assess attribution quality in generated explanations. We implement our protocol using both human annotators and automatic annotators and found that LLM annotation correlates with human annotation, suggesting that attribution assessment can be automated. Finally, our experiments reveal that: (1) the best-performing LLMs still generate explanations that are not always accurate in their attribution; and (2) human-curated evidence is essential for generating better explanations.

## introduction the dissemination of online misinformation poses a significant threat to society, with consequences ranging from seeding skepticism and discrediting science, to endangering public health and safety. to verify online claims at scale, automated factchecking systems have been proposed to classify claims based on their truthfulness @xcite . however, debunking by simply calling a claim "false" can lead to a backfire effect, whereby the belief in a false claim is further reinforced rather than hindered @xcite . as such, there's growing research on generating textual explanations to justify the outcomes of fact-checking systems using large language models (llms) in various contexts @xcite . for automated factchecking, the typical input is a claim and a list of retrieved evidence passages, from which a subset of evidence passages is selected and fed to the llm for explanation generation. ## related work automated fact-checking aims to classify the veracity of a claim @xcite . however, debunking solely by providing a classification label (e.g. false) is neither effective nor convincing, and can induce a "backfire" effect where the erroneous belief is reinforced @xcite . this motivates the task of justification or explanation generation for fact-checking. explanation generation in the literature is framed as either extractive or abstractive summarization over evidence @xcite @xcite . however, extractive methods struggle to produce annotator 9. facebook doesn't make donations based… 10. this is an example of long-running scam … the claim that … is false. it is crucial to note that facebook does not make donations … [9]. such posts are typically part of a longrunning scam … a sick child [10]. though it might initially appear benign … this increased visibility subsequently allows the scammers … further deceptions [11]. masked explanation selected evidence 11. while it may seem harmless at first … annotator prediction generated explanation the claim that … is false. it is crucial to note that facebook does not make donations … . such posts are typically part of a long-running scam … a sick child [10]. though it might initially appear benign … this increased visibility subsequently allows the scammers … further deceptions [11]. ## automatic evaluation of evidence attribution we now attempt to automate the annotation process, i.e. automate the citation recovery task in the assessment protocol (section 3.3). we experiment with two approaches: natural language inference (nli), and llm annotators. nli most existing work frames the evaluation of citation quality as an nli task @xcite . that is, given a pair of (premise, hypothesis), the nli model outputs a label indicating whether the premise entails or contradicts the hypothesis. in our scenario, the premise is an evidence passage (e k ) while the hypothesis is an explanation sentence (a sentence in x). given a selected evidence passage, we pair it with every sentence in the explanation and use a pretrained nli model to detect instances of entailment: any sentence that is predicted to be entailed by the evidence passage is added to the recovered set (x mask pred ). in terms of pretrained nli models, we use two models: debertav3 @xcite and true @xcite . 8 the output label set for debertav3 is "entailment", "neutral", and "contradiction", and for true is "1" (entailment) and "0" (not entailment). for our experiments we are only concerned with the entailment class. llm we also experiment with using an llm to do the citation recovery task. we zero-shot prompt an llm to recover the sentences (x mask pred ), using an instruction similar to what we provide to the human annotators (section 3.4). for the input, we include the evidence passage (e k ) and all explanation sentences in the prompt. see appendix in terms of llms, we use the same set of models that we used for generating explanations (i.e. gpt-4, gpt-3.5 and llama2), and further include several smaller llms: llama3.1 @xcite , mistral @xcite and gemma @xcite . 9 evaluation setting we perform automatic annotation under two settings: (1) sampling: for each claim, only one evidence passage is selected for masking -this is the approach we used in the human annotation in section 3; and (2) full: for each claim, we mask and evaluate all evidence passages, one at a time. the full setting means we are assessing the attribution accuracy for every sentence that contains a citation marker in the explanation, and providing a more complete picture of the quality of the generated explanations (noting that this wasn't feasible with human evaluation). note that in this setting, for each claim we have multiple set precision, recall and f1 scores (one for each evidence passage; section 3.3), and we aggregate them by computing the mean. ## conclusion in this paper, we explore evaluating evidence attribution for fact-checking explanations. we implement a novel protocol using both human annotation and automated methods, and show that our llm-based annotations have stronger correlation with human annotations compared to nli models. while the best-performing llm shows promising results, there is still substantial room for improvement in explanation generation. our findings highlight the importance of evidence selection, as human-curated evidence tends to produce more attributable explanations. ultimately, our research provides insights into enhancing ai trustworthiness and paves the way for future developments of transparent explanation generation for fact-checking.