C laim V er: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024)

Abstract: In the midst of widespread misinformation and disinformation through social media and the proliferation of AI-generated texts, it has become increasingly difficult for people to validate and trust information they encounter. Many fact-checking approaches and tools have been developed, but they often lack appropriate explainability or granularity to be useful in various contexts. A text validation method that is easy to use, accessible, and can perform fine-grained evidence attribution has become crucial. More importantly, building user trust in such a method requires presenting the rationale behind each prediction, as research shows this significantly influences people’s belief in automated systems. Localizing and bringing users’ attention to the specific problematic content is also paramount, instead of providing simple blanket labels. In this paper, we present ClaimVer, a human-centric framework tailored to meet users’ informational and verification needs by generating rich annotations and thereby reducing cognitive load. Designed to deliver comprehensive evaluations of texts, it highlights each claim, verifies it against a trusted knowledge graph (KG), presents the evidence, and provides succinct, clear explanations for each claim prediction. Finally, our framework introduces an attribution score, enhancing applicability across a wide range of downstream tasks.

## introduction misinformation and disinformation are longstanding issues, but the proliferation of ai tools that can generate information on demand has amplified these issues. tools for fact-checking are not keeping pace with sophisticated text generation techniques. even when they are effective, they lack appropriate explainability and granularity to be useful to users. studies have shown that explanations are crucial for users to build trust in ai systems @xcite @xcite . therefore, there is a need for a novel human-centric approach to text verification that offers usable and sufficiently granular explanations to inform and educate the user. most fact-checkers, including widely used ones in deployment, issue blanket predictions that can lead to user misunderstandings. for instance, in figure 1 (a), we observe that healthfeedback foot_0 , a fact-checker for medical text, indicates that a misleading statement about the increase in autism is inaccurate. however, there are multiple claims made in that text, which are not addressed by this tool. in fact, research does show that autism cases have increased, but this is mostly attributed to increased testing @xcite . our method accurately breaks down the text into multiple claims and shows that the specific claim that vaccines are causing autism is indeed incorrect, attributing it to a fact from the wikidata @xcite . it also provides a clear rationale as to why the first two claims cannot be determined, as there's no conclusive evidence present in the kg. such granular predictions, supported by justifications, significantly improve user confidence @xcite @xcite . similarly, in figure 1 (b), we notice that google fact check tools foot_1 provides a blanket label for an utterance denying the moon landing. in contrast, claimver identifies the exact text span that can be conclusively proven incorrect and proceeds to provide specific information about the apollo 11 mission and its crew members to refute the claim. all verified entities present in the text, along with their wiki ids and descriptions, are displayed for user reference. prior research @xcite @xcite typically validates text at the paragraph or sentence level without adequately enhancing user awareness by supplying key details such as rationale, match scores, or evidence. a kg-based approach allows for finer granularity, aiding in pinpointing specific inaccuracies like hallucinations in llm-generated text or false claims in misleading text. furthermore, if needed, broader-level metrics can be extracted from this detailed attribution. the assumption of one-to-one mapping between input and reference texts, prevalent in previous methods @xcite @xcite , does not hold if the given text consists of claims that can be mapped to more than one source. in contrast, utilizing a kg, which represents a consolidated body of knowledge, results in a more comprehensive evaluation. while most previous methods may not support scenarios with information spread across various references, querying a kg can yield triplets originally sourced from multiple documents. additionally, procuring the specific spans of text required to evaluate claims from large text sources that may span several pages presents many challenges. in contrast, a kg captures only the most important relationships as nodes and links, providing a more efficient way to evaluate the claims. ## related work research on validating text has been ongoing for the past decade, while the concept of evidence attribution has gained increased attention in recent years, following the advent of generative models. our method integrates fact verification and evidence attribution. in this section, we discuss recent advancements in both domains. ## methodology in this section, we present the methodology for retrieving relevant triplets from the kg, fine-tuning llm to process text at claim-level, verifying claims, tagging evidence for each prediction, and generating a rationale along with an attribution score that reflects the text's validity. ## dataset open-domain question answering (qa) datasets, such as wikiqa @xcite , hotpotqa @xcite , popqa @xcite , and entityquestions @xcite , as well as fact verification datasets like fever @xcite , feverous @xcite , tabfact @xcite , and sem-tab-facts @xcite , provide texts along with corresponding reference contexts or attributable information sources. however, these datasets significantly differ from the type of data required to train and test our proposed objective function, primarily due to two major factors: (i) these datasets predominantly offer samples that are inherently attributable, and (ii) consist of atomic claims and/or one-to-one mappings between input and reference texts. to address the first limitation, prior work @xcite in attribution evaluation introduced new samples by modifying correct answers to generate contradictory instances. yet, this adjustment alone is not sufficient for our use case because our method requires attribution at the claim level, and necessitates the automatic decomposition input text to claims. consequently, as this task represents a novel challenge, we developed a new dataset that enables effective training and testing of the objective function. considering the choice of our kg, which is wikidata @xcite , we opted for wikiqa @xcite as it is closely associated with the wiki ecosystem. given that our method is designed for text validation in general, and is not limited to question answering, we retain only answers and discard the questions. subsequently, we processed the answers following the steps detailed in section 3.1, selecting entries containing two or more wiki entities. this approach resulted in the exclusion of most single-word answers and other responses that are dependent on their corresponding questions and may lack comprehensibility without them. we utilize @xcite to generate the initial version of the ground truth. although gpt-4 can adhere to the instructions (refer to figure 3 ) to a reasonable degree and responds in the required format with all necessary keys, it still underperforms in the overall task. the most frequent issue observed is the erroneous assignment of prediction labels. to remedy this issue, we designed a detailed prompt tailored for the given task, incorporating techniques such as few-shot, chain-ofthought @xcite , and other strategies @xcite (full prompt in §a figure 12 ). we also conducted manual checks to ensure only high-quality samples were retained, as research indicates that high alignment can be achieved with as few as 1,000 samples, provided they are of superior quality @xcite . the final dataset is comprised of two splits: the training split, based on the training split of wikiqa @xcite , and a test split, derived from both the test and validation splits. the training split contains 3,400 samples, and since some entries feature multiple claims, there are a total of 5,342 claims within this split. similarly, the test split includes 1,000 samples and 1,677 claims. the label counts for the claims are tabulated in ## experiments and results in this section, we present the evaluation of our claim-level attribution method. the performance metrics of the fine-tuned llms, which operationalize the objective function, are presented in tables 3 and 4 . in the first task of the proposed objective function (refer §3.3), decomposing text into multiple claims, is somewhat subjective, and there could be multiple valid approaches due to linguistic complexities. for instance, example 4 in input text relevant triplets prediction (tms) rationale kas 1 1 george o'malley is a fictional character from the medical drama television series grey's anatomy, 2 which airs on the american broadcasting company (abc) in the united states. 1: [("grey's anatomy", 'characters', "george o'malley")] 2: [("grey's anatomy", 'original broadcaster', 'american broadcasting company'), ('american broadcasting company', 'country', 'united states of america')] 1: attributable (0.852) 2: attributable (0.637) 1: the triplet directly supports the claim that george o'malley is a character in grey's anatomy. 2: the triplets confirm that grey's anatomy airs on abc, which is based in the united states, directly supporting the claim about the show's broadcasting and location. 0.818 2 1 bane was portrayed as a tertiary villain by robert swenson in batman & robin , 2 directed by joel schumacher, 3 and batman's back 1: [('batman & robin', 'cast member', 'robert swenson')] 2: [('batman & robin', 'director', 'joel schumacher')] 3: na 1: attributable (0.788) 2: attributable (0.882) 3: extrapolatory (0.0) 1: the triplet directly supports the claim that robert swenson was involved in batman & robin, which is a requirement for the statement about his portrayal of bane. 2: the triplet directly supports the claim about the director of batman & robin, which is relevant to the context of the film. 3: there are no triplets that directly support or refute the claim about batman's back 0.752 3 1 crater lake is the main feature of crater lake national park 2 and famous for its deep blue color and water clarity. to accurately compute classification performance, we impose a strict strategy: the text span of the claim, the identified relevant triplets, and the prediction label must all exactly match the ground truth to be considered accurate. in in the first example in 2, the input text is decomposed into two claims, both of which are attributable. the first claim is supported by a single triplet in the kg, while the second claim can be supported by combining two triplets. the second example presents more challenges for evaluation due to its complex sentence structure, but claimver accurately identifies that the third claim regarding batman's back is neither supported nor refuted by the triplets, as indicated in the rationale. in the third example, we note that the first claim is predicted to be attributable with a high triplet match score of 0.942 since there is a triplet that clearly supports the location description of crater lake. however, as there is no information regarding the water characteristics, the second claim is categorized as extrapolatory. in the fourth example, the first claim alone requires three triplets combined as supporting evidence, illustrating the method's ability to handle complex multi-hop paths within the kg. the second and third claims are predicted to be extrapolatory, since there are no triplets concerning airbus's market share, or its activities in europe, as highlighted in the model's rationale. it is noteworthy that the context provided in the third claim is crucial for the first claim to be comprehensible, demonstrating why individual claim evaluation may be suboptimal. interestingly, in the fifth example, the method identifies a specific instance from the kg to refute a general claim, citing the appointment of rutilio del riego jáñez. similarly, in the sixth example, the method provides specific instances, quoting two distinct boeing 737 models to demonstrate contradiction with a high triplet match score. ## discussion the susceptibility of llms to generating factually incorrect statements is an alarming concern as llm-powered services become increasingly popular for seeking advice and information. the democratization of generative models has also had adverse effects, such as increasing misinformation @xcite . to arm end-users with the tools necessary to combat being misinformed, it is crucial to develop text-validation methods that are human-centric, and prioritize user engagement, understanding, and informativeness. we design our method with these principles in mind: we make predictions at the claim level, and identify text spans within the given text, that can be color-coded and presented to the user. the proposed method also generates easily comprehensible explanations along with the prediction and evidence, thus reducing the cognitive burden on the end-user, and making the process user-friendly. the usability and evaluation of these systems ## conclusion in this paper, we present claimver, a framework for text verification and evidence attribution at the claim level by leveraging information present in kgs. in contrast to other methods, claimver eliminates the one-to-one mapping between input and reference text, allowing for layered interpretation and handling of distributed information. in addition to these primary functions, claimver incorporates human-centric design principles by offering clear, concise explanations for each claim prediction-an important characteristic for building user trust and enhancing usability. furthermore, we introduce an attribution score, which enhances its applicability across a wide range of downstream tasks. finally, we share claimver fine-tuned llms to facilitate further exploration of this research direction.