Complex Claim Verification with Evidence Retrieved in the Wild (2024)

Abstract: Retrieving evidence to support or refute claims is a core part of automatic fact-checking. Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: either no access to evidence, access to evidence curated by a human fact-checker, or access to evidence published after a claim was made. In this work, we present the first realistic pipeline to check real-world claims by retrieving raw evidence from the web. We restrict our retriever to only search documents available prior to the claim’s making, modeling the realistic scenario of emerging claims. Our pipeline includes five components: claim decomposition, raw document retrieval, fine-grained evidence retrieval, claim-focused summarization, and veracity judgment. We conduct experiments on complex political claims in the ClaimDecomp dataset and show that the aggregated evidence produced by our pipeline improves veracity judgments. Human evaluation finds the evidence summary produced by our system is reliable (it does not hallucinate information) and relevant to answering key questions about a claim, suggesting that it can assist fact-checkers even when it does not reflect a complete evidence set.

## introduction to combat the rise of misinformation, the nlp community has developed automatic fact-checking tools. however, these automated systems are not ready for wide adoption at real fact-checking organizations. prior work handling real claims either relies on access to a document set which contains the "gold" evidence @xcite @xcite or conducts unconstrained retrieval @xcite , which may retrieve articles written by fact-checkers about the 1 code and data available at https://github.com/ jifan-chen/fact-checking-via-raw-evidence prior work: evidence published post-hoc our focus: raw evidence available to fact-checkers claim: james quintero stated on october 10, 2016: "when san francisco banned plas2c grocery bags, you saw the number of instances of people going to the er … spike." @xcite @xcite poli2fact found that the study methodology and conclusions, were overly simplis2c… evidence retrieval a research paper found plas2c bags bans had significant nega2ve repercussions on public health… … … figure 1: our fact-checking setting addresses realistic claims using evidence retrieved prior to when the claim was made. ## background and motivation early nlp research on fact-checking political claims @xcite @xcite @xcite typically considered using the claim alone as an input to an automated system. by not seeking evidence, systems judge the veracity of a claim mostly based on surface-level linguistic patterns rather than based on factual errors. research that incorporates evi-dence either assumes access to justifications provided by fact-checkers @xcite @xcite or evidence from unconstrained retrieval @xcite @xcite , which frequently yields evidence sets containing pages from fact-checking websites @xcite . this does not reflect the difficulties in real-world evidence retrieval. @xcite explore generating questions to retrieve evidence from the web, but only evaluate their system with humans in the loop, who can aggressively filter irrelevant retrieval results. contemporaneous to this work, @xcite construct a dataset, averitec, using real-world claims and evidence retrieved from the web. our method uses binary subquestions designed to target all needed aspects of factuality for a claim, whereas their questions are wh-questions optimized around retrieval, similar to qabriefs @xcite . to our knowledge, we present the first automatic fact-checking system with a realistic retrieval pipeline using evidence available at the time a claim was made. this presents a very challenging setting where many claims are not checkable. we therefore emphasize the evidence our system returns as a way of assisting human fact-checkers; we believe this realistic task setting and corresponding evaluation should be reused in future work. our work shifts the focus away from the evaluation on classification accuracy alone. accuracy on truth labels assigned by fact-checkers is a proxy metric we use to evaluate our systems. however, fact-checking experts argue that the task is too subjective and complex to be automated in the near term @xcite . part of this arises from the fact that information needed to check claims is not always available on the web @xcite . our approach of returning claim: james quintero stated on october 10, 2016 in a panel discussion at sxsw eco: when san francisco banned plas@c grocery bags, "you saw the number of instances of people going to the er with things like salmonella and other related illnesses" spike. ## methodology our pipeline, shown in figure 3 , consists of five parts: claim decomposition, raw document retrieval, fine-grained retrieval, claim-focused summarization, and veracity classification. we describe each part below. ## experimental setup our main automatic evaluation is on claim veracity prediction (wang, 2017), evaluating our entire pipeline end-to-end. we will describe the human evaluation setup in section 6. ## human evaluation of summaries summarizing documents from web search with large language models improves the performance of our fact-checking pipeline. however, these models can generate untruthful content @xcite @xcite . furthermore, as pointed out by @xcite , the accuracy of veracity classification alone does not entirely reflect the system's overall effectiveness, as certain labels such as "false" and "barelytrue" may be ambiguous. we believe the true measure of our system's utility lies in the full package of summarized evidence it returns rather than just the accuracy of the veracity label. therefore, we carry out two human studies, on comprehensiveness and faithfulness, to better understand intermediate outputs of the system. ## related work retrieval augmented models prior work has shown that a variety of nlp tasks could benefit from incorporating a retrieval component. such tasks mainly include question answering @xcite @xcite @xcite ), text generation @xcite @xcite , language modeling @xcite @xcite , and dialog @xcite @xcite . most of these work assume having access to a fixed corpus, however, for the task of real-world fact-checking, no such corpus exists. in this work, we follow webgpt @xcite and use bing search api to retrieve evidence from the wild web. recent llm agents such as bing chat and google bard follow this paradigm, so we believe these directions will be relevant for future work. question decomposition has been shown to be effective in evidence retrieval and question understanding for complex question answering @xcite @xcite @xcite . question generation has also been shown to play a useful role in retrieval pipelines in opendomain qa @xcite . in more recent research, it was demonstrated by @xcite that such decompositions can also aid in retrieving evidence to assess complex claims and make veracity judgment. this observation is consistent with concurrent studies on fact-checking text generation outputs @xcite @xcite and wikipedia @xcite . ## conclusion we introduce a pipeline for realistic, automated fact-checking of complex political claims by retrieving raw evidence from web documents, improving final fact checking accuracy by integrating retrieved evidence. our pipeline show promising results on the claimdecomp dataset. yet, web search often cannot surface all the pieces of information necessary to verify a given claim. this work emphasizes the challenges of evidence retrieval in real-world scenarios and underscores the need for a human-in-the-loop fact-checking system.