Venue: NAACL 2024

# Complex Claim Verification with Evidence Retrieved in the Wild

Jifan Chen Grace Kim Aniruddh Sriram Greg Durrett Eunsol Choi

Department of Computer Science

The University of Texas at Austin jf_chen@utexas.edu

###### 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.1

Footnote 1: Code and data available at [https://github.com/jifan-chen/Fact-checking-via-Raw-Evidence](https://github.com/jifan-chen/Fact-checking-via-Raw-Evidence)

## 1 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 Ferreira and Vlachos (2016); Alhindi et al. (2018); Hanselowski et al. (2019); Atanasova et al. (2020) or conducts unconstrained retrieval Augenstein et al. (2019), which may retrieve articles written by fact-checkers about the claim (example in Figure 1).

We present the first study of fact-checking political claims under a realistic retrieval setting. Our retrieval over the web is restricted to documents authored before the time of the claim and not sourced from fact-checking websites, as shown by the left side of Figure 1. We propose a pipeline (illustrated in Figure 2) that builds upon prior work in fact checking as well as large language models Brown et al. (2020) to handle the complexity of this setting. Our system first decomposes a claim into a series of subquestions Chen et al. (2022a); Ousidhoum et al. (2022), targeting both explicit and implicit aspects of the claim. Each subquestion is fed into a commercial search engine to retrieve relevant documents, with the restrictions described above. Then, we conduct a second stage of fine-grained retrieval to isolate the most relevant portions of the documents. Finally, we use state-of-the-art language models Brown et al. (2020); Ouyang et al. (2022) to generate claim-focused summaries from the retrieved content. These summaries can serve both as explanations for users as well as inputs to a classifier to determine the veracity based on these summaries.

Evaluating individual components of our

Figure 1: Our fact-checking setting addresses realistic claims using evidence retrieved prior to when the claim was made.



pipeline is challenging due to the absence of gold annotations at each stage. We use automatic evaluation on the veracity classification performance, comparing to labels given by professional fact-checkers. We supplement this with a human study evaluating the claim-focused summaries for comprehensiveness and faithfulness. This evaluation counterbalances the subjectivity of the veracity judgments Lim (2018) while shedding light on intermediate stages of the process.

We apply our pipeline to ClaimDecompChen et al. (2022), a dataset containing 1,200 real-world complex political claims with veracity labels. Performance on veracity classification shows that: (1) our retrieval setting is indeed much harder than "unrestricted" retrieval settings; (2) using web evidence leads to performance gains compared to automatic fact-checking without evidence; (3) the decomposition is crucial for obtaining high-quality raw documents from the web compared to using the original claim alone. Our human study further indicates that: (4) claim-focused summaries are mostly faithful and helpful for both machines and humans to fact-check a claim; (5) the retrieved evidence is often relevant to some aspects of the claim, but can rarely cover all aspects, suggesting that finding sufficient raw evidence in the wild is the core challenge in building automatic fact-checking systems.

## 2 Background and Motivation

Early NLP research on fact-checking political claims Vlachos and Riedel (2014); Wang (2017); Rashkin et al. (2017); Volkova et al. (2017); Perez-Rosas et al. (2018); Dungs et al. (2018) 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 evidence either assumes access to justifications provided by fact-checkers Vlachos and Riedel (2014); Alhindi et al. (2018); Hanselowski et al. (2019); Atanasova et al. (2020) or evidence from _unconstrained_ retrieval Popat et al. (2017, 2018); Augenstein et al. (2019), which frequently yields evidence sets containing pages from fact-checking websites Glockner et al. (2022). This does not reflect the difficulties in real-world evidence retrieval. Fan et al. (2020) 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, Schlichtkrull et al. (2023) 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 Fan et al. (2020).

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 Graves (2018); Nakov et al. (2021). Part of this arises from the fact that information needed to check claims is not always available on the web Singh et al. (2021). Our approach of returning

Figure 2: Overview of our pipeline: a claim is decomposed into yes/no subquestions (Sec. 3.1), then we use the questions in two stages of retrieval (Sec. 3.2 and Sec. 3.3) to select the most relevant paragraphs. Finally, we generate a claim-focused summary (Sec. 3.4) and train a veracity classifier to get the veracity label (Sec. 3.5). This filters contents irrelevant to the claim (see Appendix B for details and an example in Figure 5).

 

information on a best-effort basis and providing evidence to enable humans to assist in the judgment can help overcome issues with returning judgments from error-prone AI systems (Bansal et al., 2021; Brand et al., 2022).

## 3 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.

### Claim Decomposition

Given a real-world complex claim, we first decompose it into a set of yes/no questions for which the answers are useful to fact-check the claim. Chen et al. (2022a); Ousidhoum et al. (2022) show that such decompositions are both helpful to retrieve relevant evidence and make veracity judgments.

For decomposition, we prompt a large-scale language model, text-davinci-003, with inncontext examples.2 We carefully choose four input-decomposition pairs from the human annotations of Chen et al. (2022a) to form a few-shot prompt. We generate a set of questions through multiple rounds of sampling until we gather 10 different questions. An example decomposition is shown in Figure 3. For the full prompt, see Appendix A.2.

Footnote 2: During a pilot study, we compared the questions generated from text-davinci-003 and the questions generated using the fine-tuned T5-3b model from Chen et al. (2022a) and we found that the questions generated by text-davinci-003 are more diverse and comprehensive.

### First-stage Retrieval

For each question generated in the previous step, we feed it to a commercial search engine API to collect the relevant documents.

Temporal and Site ConstraintsWe assume that **a system should not be able to access pages published after the claim was made.** This condition matches real-time fact-checking scenario during a political speech. We place a **temporal constraint** on the system to reflect this. Next, to investigate how the presence of fact-checking websites affects the veracity judgment of a claim, we also place a **site constraint** to filter out the documents from fact-checking websites. Our list of fact-checking websites can be found in Appendix A.1. An example of the retrieved documents is shown in Figure 4.

We use the Bing Search API,3 and retrieve 10 documents per subquestion after filtering by the constraints. We extract the actual content from the page URLs using two tools: html2text4 and readability-lxml.5 Approximately one-third of the URLs are protected6 and cannot be scraped.

Footnote 3: [http://www.microsoft.com/en-us/bing/apis/bing-web-search-api](http://www.microsoft.com/en-us/bing/apis/bing-web-search-api)

Footnote 4: [https://github.com/Alirj3z4/html2text/](https://github.com/Alirj3z4/html2text/)

Footnote 5: [https://github.com/buriy/python-readability](https://github.com/buriy/python-readability)

Table 1 contains the raw counts from web retrieval with and without the timestamp of a claim. These results underscore the importance of temporal filtering: we find little overlap between the two document sets by comparing the Jaccard distance between two sets of the retrieved URLs.

One challenge for the reproducibility of our work

Figure 4: Two documents returned by searching Q2 (generated in step 1). The right page post-dates the claim by one month and directly cites a PolitiFact article, making it problematic to use as raw evidence.

Figure 3: An example of our claim decomposition process: each claim is decomposed into ten subquestions.

 

is that commercial search engines may return different results over time. In Section 5.3, we experiment with the same query set at different times. We find that the search results change over time: only 30% of search output URLs overlap when queried two months apart. However, the veracity judgment classification result is not impacted much.

### Second-stage Retrieval

Most of the documents collected from the previous step contain at most a few snippets relevant to the claim. However, as can be seen from Figure 2, first-stage retrieval can easily result in tens of thousands of words of retrieved documents, which are costly to process with an LLM. Furthermore, even with state-of-the-art language models, it is hard to do complex reasoning over such long context Liu et al. (2024); Levy et al. (2024). Thus, we conduct a second-stage retrieval to pick the most relevant text spans to the claim from the retrieved documents. Specifically, we segment the documents into text spans containing \(k_{1}\) words with a stride of \(\frac{1}{2}k_{1}\) words. Following Chen et al. (2022a), we employ BM-25 to retrieve the top-\(K_{1}\) highest-scored text spans, expanding these spans with a \(\pm k_{2}\)-word context. If two text spans overlap, they are merged to form a larger span. This process yields a set of "documents" ranked by the highest-scored text spans, of which we pick the top-\(K_{2}\).

### Claim-Focused Summarization

Since the documents retrieved in the previous step can contain up to several thousand words, it b

Figure 5: System outputs for an example picked from the dev set of ClaimDecomp: the claim is first decomposed into a set of yes/no questions and then the top four retrieved documents (through first and second stage retrieval) are summarized. Finally, a trained DeBERTa model makes a prediction regarding the four summarized documents.

\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \# retrieved & \# scraped & \# words \\ \hline w/ timestamp & 66.7 & 45.0 & 1,561 \\ w/o timestamp & 70.4 & 47.8 & 1,660 \\ Jaccard score & 0.12 & 0.12 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics for the retrieved documents obtained through the first-stage retrieval after filtering the documents from fact-checking websites. Jaccard between these two sets show that incorporating the timestamp in retrieval makes a substantial difference.

 

comes cumbersome for both humans and models to make a judgment based on them (Stammbach and Ash, 2020). Consequently, we prompt a large language model, specifically text-davinci-003, to summarize each retrieved document _separately_ with respect to the claim.7 Such single-document summarization has been shown to be robust on news articles Goyal et al. (2022); Zhang et al. (2023).

Footnote 6: [https://github.com/facebook/language-model-with-the-trained-learning-based-to-use-themed-in-sequence-for-instance-of-the-materials-and-machine-on-the-stations-antimodal-task-for-the-training-and-the-tour-casing-through-the-sourced-challenge](https://github.com/facebook/language-model-with-the-trained-learning-based-to-use-themed-in-sequence-for-instance-of-the-materials-and-machine-on-the-stations-antimodal-task-for-the-training-and-the-tour-casing-through-the-sourced-challenge)

We investigate two types of prompts. For a **zero-shot** prompt, we instruct the model not to make any judgments about the stance of the given document. For a **few-shot** prompt, we select four documents and carefully write desired summaries. For documents that are not relevant to the claim, we write "the document is not relevant to checking the claim" as its desired output. We conduct human evaluation of the summary quality of different prompts in Section 6.1, where we find that few-shot prompting works better. See Appendix A.3 for full prompts.

### Vercacity Classification

The final stage of our pipeline involves making a judgment based on the summaries generated in the previous stage. Unlike previous stages which use off-the-shelf tools, here we _train_ a DeBERTalarge He et al. (2020) model8 to perform a six-way veracity classification (true, mostly true, half true, barely true, false, and pants-on-fire).

Footnote 7: During a pilot study, we explored prompting text-davinci-003 to generate one summary using all documents. However, it frequently went beyond simple summarization and produced verdicts such as “therefore, the claim is refuted by the document,” which were unreliable compared to using our veracity classifier.

TrainingWe run our pipeline over the training, development, and test data of ClaimDecomp and train on pairs of the form (claim+summary, label). Since the dataset is small, we train the classifier five times with different random seeds and report the test set performance using the model that achieves the best performance on the development set.

### Final Pipeline

Our complete pipeline's results when executed on an example are shown in Figure 5. We note that the question decomposition phase yields an overcomplete set of questions, including redundant ones. However, the final retrieved and summarized documents are able to shed light on the claim from several complementary perspectives. While the final veracity judgment does not exactly match the judgment from PolitiFact, reading the documents still gives an informed picture of the situation.

## 4 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.

DataWe use the data from ClaimDecompChen et al. (2022a) which contains 1,200 complex claims from PolitiFact (train: 800, dev: 200, test: 200). Each claim is labeled with one of the six veracity labels, a justification paragraph written by expert fact-checkers, and subquestions annotated by prior work.

HyperparametersFor the second-stage retrieval, we set top-\(K_{1}=10\) (highest-scored text spans), top-\(K_{2}=4\) (highest-scored documents), \(k_{1}=30\) (chunk size), and \(k_{2}=150\) (expansion parameter). See appendix A.4 for all hyperparameters.

Evaluation MetricWe report accuracy (Acc), mean absolute error (MAE, on our 6-point scale), and Macro-F1. We also introduce soft accuracy (soft Acc), which is calculated by counting off-by-one errors on the six-point veracity scale (e.g., _half true_ instead of _mostly true_) as correct, as veracity judgments are subjective.

Comparison SystemsFor our **Claim-only** system, we concatenate the metadata, including the speaker and the venue of the claim, with the claim itself, and feed the resulting text into the classifier Wang (2017). This approach serves as a lower bound for the veracity classification.

We extend the Claim-only baseline to **Claim+Justification** by appending the human-written justification paragraph, excluding the sentence containing the label, to the claim. This is an oracle setting to establish an upper bound for veracity classification.

## 5 Automatic Evaluation: Claim Veracity

### Constrained vs. Unconstrained Search

We first situate our work with respect to baselines and past systems by varying the retrieval condition. We experiment with a **temporal** constraint, where pages must originate before the date of the claim, and a **site** constraint, where sites must be 

non-fact-checking (non-FC) sites. Even in the unconstrained setting, we exclude pages from PolitiFact (our dataset's source) to prevent label leakage.

The unconstrained setting corresponds to that used in MultiFC Augenstein et al. (2019). **MultiFC includes numerous documents that are filtered out by our constrained settings.** For each claim, they extract the top 10 pages from the Google search API. We find that 12,721 out of 15,379 claims (82.7%) contain at least one page from our excluded website list and 24.4% of the retrieved web pages are from fact-checking websites.

Table 2 reports the performance of our system with various retrieval constraints. Comparing the performance of _claim-only_ and other models that use retrieval, we see a statistically significant9 improvement over all four of our metrics in nearly all settings, showing that **retrieving and summarizing evidence is helpful to predict the veracity label, even with constraints**.

Footnote 9: Throughout our study, we use paired bootstrap tests for statistical significance between the results.

Second, we see **adding either temporal or site constraints dramatically reduces the performance**. This implies that retrieval over the web works largely because it retrieves fact-checks that were published after the claim was released, with synthesized evidence. We believe that future work on retrieval should use a constrained setting.

### Stage Ablations

We evaluate design choices in each stage of the pipeline to understand how each individual component contributes to the final performance. The results are shown in Table 3.

First-stage Retrieval: subquestions vs. original claimUsing the original claim instead of the generated subquestions as an input to web search (P vs. 1) results in a notable decrease in performance. The subquestion set encompasses multiple aspects of the claim, enabling the search engine to locate relevant information more easily across separate search queries. Comparing P and Q, we see using the gold subquestions actually yields worse performance than our predicted subquestions. This could be because we predict 10 subquestions, potentially garnering more relevant data than the 3 (on average) gold subquestions Chen et al. (2022a).

Second-stage RetrievalRather than retrieving with subquestions (subQs), we instead perform our search with the raw **Claim** (Q), **Gold subQs** from ClaimDecomp (Q), or **Justification** (S), which uses oracle information. Different queries yield only slight differences in performance and none of them is statistically significant, even when S uses the human-written justification. We believe this is because we expand the retrieved text span by a context window (\(\pm 150\) words). As a result, this retrieval step does not need to be very precise to capture the relevant information.

Claim-focused SummarizationWe compare **zero-shot** (P) and **few-shot** (Q) prompts for generating the summary; **no summary** (Q) directly feeds the text spans from second-stage retrieval to the veracity classifier. System T shows the worst performance across all metrics, suggesting that summarization matters. This may result from two primary factors: (1) The document length exceeds the context window capacity of DeBERTa, causing crucial information to be truncated. (2) our veracity classifier cannot easily discern the most relevant information given a large amount of context. Differences in the prompt (B and C) do not impact veracity classification results much but have differences under human inspection, which we discuss in the next section.

\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{Retrieval Constraint} & \multicolumn{6}{c}{Dev (N=200)} & \multicolumn{6}{c}{Test (N=200)} \\ Temporal & Site & Acc & Soft Acc & Macro-F1 & MAE & Acc & Soft Acc & Macro-F1 & MAE \\ \hline - & - & 50.5 & 88.5 & 47.5 & 0.62 & 49.0\({}^{+}\) & 86.0\({}^{+}\) & 48.5\({}^{+}\) & 0.68\({}^{+}\) \\ - & Non-FC & 37.5 & 76.5 & 38.6 & 0.94 & 33.5\({}^{+}\) & 75.0\({}^{+}\) & 33.9\({}^{+}\) & 0.95\({}^{+}\) \\ Before & - & 42.5 & 75.0 & 41.7 & 0.87 & 33.5\({}^{+}\) & 72.0 & 38.0\({}^{+}\) & 0.98\({}^{+}\) \\ Before & Non-FC & 40.5 & 76.5 & 41.4 & 0.87 & 33.0\({}^{+}\) & 74.5\({}^{+}\) & 34.5\({}^{+}\) & 0.99\({}^{+}\) \\ \hline \hline   Claim only & 37.0 & 71.0 & 34.6 & 0.98 & 25.5 & 68.0 & 27.5 & 1.12 \\ Claim + Justification (oracle) & 52.5 & 88.5 & 54.5 & 0.64 & 57.5 & 93.0 & 57.8 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Veracity classification performance with different retrieval constraints. The top block is our full system (P⃝ setting in Table 3) with constraints over what is retrieved. Red indicates using oracle information. “+” denotes that the results are statistically significant improvements (\(p<0.05\)) compared to the results of Claim only on the test set.

 



### Stability of First-stage Retrieval

As commercial search engines evolve over time, we conduct experiments to explore the reproducibility of our first-stage retrieval step. We use the default system setting in Table 3 and conducted three rounds of retrieval at \(T=0\), \(T=1\;{\text{week}}\), and \(T=2\;{\text{months}}\). We evaluate the Jaccard similarity of the sets of URLs retrieved from our queries to understand how much changes in the Bing API and the broader web change our results. We also evaluate the veracity of our system. Note that this Jaccard similarity is between the members of the URL sets (i.e., the URLs themselves), not capturing any lexical or domain similarity of the URLs.

Results are shown in Table 4. A noticeable trend is a decline in the Jaccard score between varying retrieval rounds over time. However, this decrease does not significantly impact the models' efficacy in the veracity assessment.

We caution that as the time gap increases, the set of documents retrieved from the Bing Search API could become considerably different, posing a challenge to consistently benchmark retrieval performance using commercial search engines. Therefore, we advocate for future research to focus on developing a comprehensive yet challenging document set that could be publicly released as a benchmark to spur research.

## 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 Bommasani et al. (2021); Chowdhery et al. (2022); Ouyang et al. (2022). Furthermore, as pointed out by Lim (2018), 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 comprehensnessness and faithfulness, to better understand intermediate outputs of the system.

SettingWe randomly pick 50 claims which contain 200 document-summary pairs from the devel

\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & Overlap & Acc & Soft-Acc & Ma-F1 & MAE \\ \hline Ours & - & 33.0 & 74.5 & 34.5 & 0.99 \\
1 week & 0.48 & 33.5 & 74.0 & 36.8 & 0.98 \\
2 months & 0.30 & 29.5 & 73.5 & 32.3 & 1.03 \\ \hline \end{tabular}
\end{table}
Table 4: Model performance with respect to different rounds of retrieval at intervals of one week and two months. The overlap between “Ours” and subsequent document sets, measured with Jaccard score, decreases as the time gap increases. However, none of the changes in our downstream metrics is statistically significant.

\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multicolumn{2}{c}{Evidence Generation} & \multicolumn{4}{c}{Performance} \\  & SSR & Summary & Acc & Soft Acc & Macro-F1 & MAE \\ \hline \multirow{5}{*}{
\begin{tabular}{} \end{tabular} } & Claim only & 25.5\({}^{+}\) & 68.0\({}^{+}\) & 27.5\({}^{+}\) & 1.12\({}^{+}\) \\  & Claim + Justification & 57.5\({}^{+}\) & 93.0\({}^{+}\) & 57.8\({}^{+}\) & 0.50\({}^{+}\) \\ \hline \multicolumn{6}{c}{Our Default System} \\ \cline{2-7}  & subQs & subQs & zero-shot-003 & 33.0 & 74.5 & 34.5 & 0.99 \\ \hline \multicolumn{7}{c}{Ablation on first-stage retrieval} \\ \cline{2-7}  & Claim & 24.5\({}^{+}\) & 71.5 & 18.0\({}^{+}\) & 1.15\({}^{+}\) \\
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opment set of ClaimDecomp and run two human evaluation studies on this set. For each task, we recruited annotators from Amazon Mechanical Turk with a qualification test. In total, we recruited 17 worker for the faithfulness study and 15 workers for the comprehensiveness study. The details about crowdsourcing can be found in Appendix C.

Comparison SystemsWe compare the summaries generated from two prompts, zero-shot-003 and few-shot-003, on GPT-3.5 (davinci-003). For the faithfulness study, we also compare the summaries generated through with zero-shot prompt on an earlier GPT model (davinci-001) (zero-shot-001) to see how the faithfulness varies for different models.

### Faithfulness Evaluation

GoalWe assess the frequency and degree to which the language model generates untruthful content during query-focused summarization. For each document and summary pair, annotators choose one of four labels below (see appendix C.1 for examples):

* **Faithful:** the summary accurately represents the meaning and details of the original document.
* **Minor Factual Error:** some details are not aligned with the original document, but the overall message remains intact.
* **Major Factual Error:** there are factual errors that result in the summary misrepresenting the original document.
* **Completely Wrong:** the language model hallucinates content that completely alters the meaning of the original document.

In addition to selecting a label, we ask annotators to provide a natural language justification for their choices. The annotations agree with a Fleiss Kappa score of 0.30. While this number is somewhat low, when we evaluated their justifications and we find many of the disagreements are because of subjectivity on the extent of factual error. We compute a consensus annotation via majority vote. We assign numerical scores to each label, where "Faithful", "Minor", "Major", and "Completely Wrong" correspond to 4, 3, 2, and 1 respectively and report average values. If all annotators disagree, we compute the average score and return the label that is nearest to the average score as a consensus.

ResultsThe results are shown in Table 5. We see that **few-shot prompting substantially decreases the chance of hallucinations in the summaries**. When combining "Factual" and "Minor", we see 89% of the summaries are good enough to be used as evidence for the classifier. Additionally, by checking the unfaithful summaries, we find that they do not consist of useful hallucination like making a veracity judgment based on the parametric knowledge. Comparing the performance of zero-shot-001 and zero-shot-003, we find that the weaker model makes more major factual errors. Together, they indicate that with stronger models and better prompts, we may expect these summarization models to improve further.

### Comprehensiveness Evaluation

GoalWe aim to measure the extent to which the claim-focused summaries are able to address the claim. This is subjective and difficult task to evaluate. Here, we leverage the human-annotated yes/no subquestions from ClaimDecomp as a proxy for evaluating the comprehensiveness of our summaries: if provided summary can help humans to answer more of these yes/no questions, we deem the summary to be more comprehensive.

In this task, annotators are given a summary / subquestion pair and label subsection as "answerable", "partially answerable",10 or "unanswerable", and additionally provide yes/no answer if the question is labeled as "answerable". Annotators were also asked to provide natural language justification for their answers. We collect this annotation on 161 questions associated with 50 claims. The annotations agree with a Fleiss Kappa score of 0.32.

Footnote 10: Sometimes the questions cannot be directly answered but can be inferred from the content of the summaries, or the summary at least contains relevant information. In such cases, we ask annotators to choose “partially answerable”.

ResultsThe results are presented in Table 6. We see that zero-shot summaries yield more answerable questions than few-shot summaries. However, faithfulness evaluation hints that this is caused by hallucinations in zero-shot summaries; the system

\begin{table}
\begin{tabular}{l c c c c c} \hline \hline Summ-type & F & Minor & Major & NF & Avg score \\ \hline zero-shot-001 & 65.8\% & 9.2\% & 20.0\% & 5.0\% & 3.45 \\ zero-shot-003 & 66.0\% & 18.0\% & 16.0\% & 0.0\% & 3.50 \\ few-shot-003 & 82.5\% & 6.5\% & 8.5\% & 2.5\% & 3.69 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Faithfulness Human Evaluation (\(N=200\)). “F” denotes that the summary is factual and “NF” denotes that the summary is completely wrong. Few-shot prompting helps the model make fewer factual errors.

 

imputes information that seems to help, but which is not supported by the document.

Nevertheless, the few-shot summaries allow us to partially address over 60% of the **gold annotated** subquestions derived from the PolitiFact justification. We find this result encouraging: even though the system does not have access to these (often subtle) factors, it can retrieve information to enable a human annotator to make a judgment about them.

### Combined Evaluation

While in previous section we evaluated faithfulness and comprehensiveness separately, here we conduct a **claim-level** evaluation: how many claims can be comprehensively addressed with a set of faithful summaries? We label a claim as **answerable** if all of its subquestions are answerable. If all subquestions are unanswerable the claim is **unanswerable**. Otherwise, we label claim as **partially unanswerable**. For claim-level faithfulness, we apply the same principles: a claim is faithful is all summaries are faithful, otherwise it is either unfaithful or contains minor factual errors. Table 7 shows the results by combining the two factors. We see that addressing every aspect of complex claims is still challenging: 36 out of 50 claims contain at least one unanswerable question. For claims that can be fully addressed (all questions are either answerable or partially answerable), only 1 out of 14 contains a major factual error in the summary.

## 7 Related Work

Retrieval augmented modelsPrior work has shown that a variety of NLP tasks could benefit from incorporating a retrieval component. Such tasks mainly include question answering Chen et al. (2017); Kwiatkowski et al. (2019); Karpukhin et al. (2020); Khattab et al. (2021); Nakano et al. (2021), text generation Lewis et al. (2020); Shi et al. (2023); Ram et al. (2023), language modeling Guu et al. (2020); Khandelwal et al. (2020); Zhong et al. (2022), and dialog Moghe et al. (2018); Fan et al. (2021); Thoppilan et al. (2022).

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 Nakano et al. (2021) 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 decompositionhas been shown to be effective in evidence retrieval and question understanding for complex question answering Talmor and Berant (2018); Min et al. (2019); Qi et al. (2019); Perez et al. (2020); Wolfson et al. (2020); Geva et al. (2021). Question generation has also been shown to play a useful role in retrieval pipelines in open-domain QA Sachan et al. (2022). In more recent research, it was demonstrated by Chen et al. (2022) 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 Gao et al. (2022); Chen et al. (2022); Liu et al. (2022) and Wikipedia Kamoi et al. (2023).

## 8 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.

\begin{table}
\begin{tabular}{l c c c} \hline \hline Summ-type & Ans & Partially Ans & UnAns \\ \hline zero-shot+003 & 47.8\% & 22.4\% & 29.8\% \\ few-shot+003 & 42.9\% & 21.1\% & 36.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Human evaluation results on 161 subquestions from the same 50 claims we picked for the human study on faithfulness. “Ans”, “Partially Ans”, and “UnAns” denote the number of questions that are answerable, partially answerable, and unanswerable.

\begin{table}
\begin{tabular}{l l l r r} \hline \hline  & FaithI & Minor & Unfaitful & Total \\ \hline Ans & 4 & 2 & 0 & 6 \\ Partially Ans & 6 & 1 & 1 & 8 \\ Partially UnAns & 13 & 5 & 11 & 30 \\ UnAns & 5 & 1 & 0 & 6 \\ \hline Total & 28 & 10 & 12 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 7: **Claim-level** statistics of Few-shot-003 taking both faithfulness and comprehensiveness into consideration. “Unfaithful” label aggregates “Major Error” and “Completely Wrong” labels. The claim-level labels are derived from the sub-parts as defined in section 6.3.

 



### Limitations and Future Directions

Performance is bottlenecked by the first-stage retrieval.The results in the last section show that 36.0% of questions are unanswerable using our most faithful claim-focused summaries. By investigating the unanswerable cases, we see that the following cases lead to retrieval failure: (1) no relevant information is available on the web except the fact-checking websites. These claims can be onerous to check, such as requiring talking to or emailing specific people to check facts. Those cases are beyond the scope of this work and we think a system doing triage for the claims, would be promising for future work. (2) No relevant subquestions are generated or the subquestions are not well decontextualized Choi et al. (2021). In such cases, a stronger question generation model or decontextualization model can help further.

The need of human-in-the-loop fact-checking.To address the failures in the first-stage retrieval and the potential errors in the summarization stage, we envision a human-in-the-loop fact-checking system. This system begins with the automated pipeline presented in this paper, which provides fact-checkers with summarized documents and judgments. If the fact-checkers deem these documents unsatisfactory, the system reveals the subquestions used for evidence retrieval, allowing fact-checkers to rerun the search. The system then retrieves additional documents and generates updated summaries. This iterative process continues until the fact-checkers are satisfied with the retrieved evidence. Moreover, the system could further learn from the fact-check feedback to improve itself: for example, the system could learn what questions are important to retrieve good evidence and what questions are not according to the fact-checker. In general, we believe such systems will be necessary, but developing them is outside of the scope of this work.

Scope of facts checked.Our work only addresses English-language political claims. Misinformation in other languages is a crucial problem that we believe future work should address. Moreover, even within English, there is a strong need for fact-checking systems that can address other kinds of claims that have a different distribution; for example, claims from social media, which are often embedded in images or memes. Nevertheless, we believe the decomposition and retrieval approach here can play a role in such systems as well.

## Acknowledgments

This work was partially supported by NSF CAREER Award IIS-2145280, by Good Systems,11 a UT Austin Grand Challenge to develop responsible AI technologies, and by grants from Salesforce Inc. and Open Philanthropy. We thank the UT Austin NLP community for feedback on the earlier drafts of the paper.

Footnote 11: [https://goodsystems.utexas.edu/](https://goodsystems.utexas.edu/)

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## Appendix A Experimental Details

### List of Websites being Filtered

* www.politifact.com
* www.snopes.com
* www.factcheck.org
* www.washingtonpost.com/news/fact-checker/
* www.apnews.com/hub/ap-fact-check
* www.fullfact.org
* www.reuters.com/fact-check

We also filter the URLs that contain "fact-check" or "factcheck"; we also filter any PDF files and videos 

Claim: Virr image stationed on June 8, 2020 in post on Facebook: COS in Norway: require a years of training, a people killed since 2002. Cops in Finland: require a years of training, 7 people killed since 2000. Cops in Iceland: require a year of training, 1 person killed since ever. Oops in the U.S.: require 21 weeks of training, 8,000+ people killed since 2001.

Suppose you are a fact-checker, generate several yes or no questions to help me answer if this claim is true or false.

Quections: Does Norway require 3 years of training for cops? Have Norwegian coins del 

Documentative by which this state allows discrete (with: "Mailing" for the constraints as an active. In response to the coronavirus, nearly half at all states expanded access to real halts for their partners), either by looking for the correctness or a reason of correctly ordering application or should be everywhere registered or/or fewer than action for the general election. As we have become increasingly written and oblivious to implement. Together with memory has also numerous unintended data that must in turn will require us configured and fraud. This supports are out of view of news the views of election events and may within both party-wise but simple tweiled parties. A recent analysis by the Washington Post found only 322 ones of potential fraud or other 194 million policies when wall in 2018 do not 2018. Quantitative agreement with these requirements is given by what Does it really lead to breat them could win.

Summers 1 QQuality quarter of success and made about 2018, and they mostly included a handful of states. Nearly everyone who asked to Congress, first starts to issue all bullets by mall is 2020, did so hard. An item was clear, how long to prevent outliers of interest of 4.7% as a host possible quadruple yet harmful? Is something bad himself ruled rules using up to 2018 percentage of votes per visit in 2018 participants between its state that benefit needs interesting even easier, the number of hidden requests is still expected to violate. To meet this challenge, local elections official will have no overcome names marked with little time and money to game. They must acquire large volumes of specialized envelopes and paper. Additional staff, and in some cases machines, see necessary to open, or tableau tools and verify.

[MISSING_PAGE_POST]

 

question in the actual task.

The detailed instructions and the annotation interface is shown in Figure 11.

## Appendix D Using LLMs as a Veracity Classifier

We experiment with using ChatGPT (gpt-3.5-turbo) as the classifier in the final stage. Since ChatGPT is not trained on our training set, it does not have access to the label distribution of the dataset. To make a fair comparison with the DeBERTa model, instead of directly predicting a discrete label (one out of the six labels), we prompt the model to explain its reasoning process and predict a truthfulness score on a scale of 0 to 100, 0 for the claim being false and 100 for true. We then rank the examples according to the predicted scores and map the scores to discrete labels to the label distribution of the training set. To be specific, we rank the examples in the training set by their labels, assigning the lowest rank to _pants-on-fire_ and the highest to _true_. Each label, denoted as \(l_{i}\), corresponds to a percentile \(p_{i}\). We then map the predicted score falling between \(p_{i}\) and \(p_{i+1}\) to the label \(l_{i}\). We use a zero-shot prompf12 to produce the score and the prompt is shown in Figure 9.

Footnote 12: We also experimented with few-shot prompts. However, these did not yield better performance than the zero-shot prompt.

The results are shown in Table 9. Comparing the **claim-only** results from the two models, we see that ChatGPT achieves slightly better performance than DeBERTa. However, unlike the DeBERTa model, when adding the summary, we see a notable performance drop for ChatGPT. We argue that this might be because ChatGPT relies heavily on prior knowledge and it is not able to use the provided summary effectively. We believe improving this is a promising direction for future work.

Figure 9: Zero-shot prompt for Claim + summary





[MISSING_PAGE_POST]

 


Figure 11: Interface of the comprehensiveness study we conducted in Section 6.2.

 



**Claim: Facebook posts stated on September 29, 2021: The White House"created a fake set for [President Joe] Bilden to get his booster shot. The entire Biden presidency is one giant charade.

* PEOPLE.com Content:** President Joe Bilden received his COVID-19 hooster shot ... Bilden received his first dose of Pfizer-BioINTech novel coronavirus vaccine in December at Delaware 's ChristianCare Hospital. His second shot was administered there three weeks later. Both shots were televised ... Summary: On Monday, President Joe Bilden received his COVID-19 booster shot on camera, administered by a nurse with the White House Medical Unit. Biden has previously received both of his initial doses of the Pfizer-BioINtech novel coronavirus vaccine in December at Delaware's ChristianCare Hospital. Minor Error: The summaries says both shots are received in December while the document says the first one was in December and the second one was three weeks later. The summary slightly changes the information in the original document. Claim: Elizabeth Warren stated on November 20, 2019 in a debate: "Today in America -
- a new study came out -
- 20 years out, whites who borrowed money, 94% of them have paid off their student loan debt; 5% of African Americans have paid it off." Document Title: More Evidence Student Borrocures Prep Their Loans
- Forbes Content: Most borrowers will still fully pay off their loans before they are required to do so ...Borrowers pay off almost all loans ( 94 %) with a final payment larger than the scheduled monthly payments they are required to make ... Summary: A new report from the Consumer Financial Protection Bureau found that 94% of borrowers pay off their student loans before the scheduled payoff date, typically going as in five years or less .... Major error: The documentation says borrows pay off 94% of the loan not 94% of borrowers pay off their loans Claim: Andrew Giuliani stated on May 18, 2021 in a news conference: "The one good thing about the antibodies if you've had it, is it actually is even better than the vaccine, and here's why. With the vaccine you can still transmit, with the antibiotics you can't transmit." Document Title: COVID-19: Long-term effects
- Mayo Clinic Content: It involves extreme fatigue that worsens with physical or mental activity , but doesn't improve with rest ...What should you do if you have post-COVID-19 syndrome symptoms ? If you’re having symptoms of post-COVID-19 syndrome , talk to your health care provider ... Summary: The Centers for Disease Control and Prevention states that there is no evidence to suggest that people who have recovered from COVID-19 and have antibodies are not able to transmit the virus. Completely Wrong: The document is about the long-term effects of COVID-19. However, model is likely utilizing its parameterized knowledge and draws the conclusion directly.

Figure 12: Three examples from the faithfulness evaluation (Section 6.1), showing the cases of minor error, major error, and completely wrong, respectively. Red text denotes the mismatches between the summary and the document.