Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments (2024)

Abstract: The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim credibility. Nevertheless, existing methods heavily rely on the embedded knowledge within LLMs and / or black-box APIs for evidence collection, leading to subpar performance with smaller LLMs or upon unreliable context. In this paper, we propose retrieval augmented fact verification through the synthesis of contrasting arguments (RAFTS). Upon input claims, RAFTS starts with evidence retrieval, where we design a retrieval pipeline to collect and re-rank relevant documents from verifiable sources. Then, RAFTS forms contrastive arguments (i.e., supporting or refuting) conditioned on the retrieved evidence. In addition, RAFTS leverages an embedding model to identify informative demonstrations, followed by in-context prompting to generate the prediction and explanation. Our method effectively retrieves relevant documents as evidence and evaluates arguments from varying perspectives, incorporating nuanced information for fine-grained decisionmaking. Combined with informative in-context examples as prior, RAFTS achieves significant improvements to supervised and LLM baselines without complex prompts. We demonstrate the effectiveness of our method through extensive experiments, where RAFTS can outperform GPT-based methods with a significantly smaller 7B LLM1.

heading: 1 Introduction
text: As the scope of social media and digital forums continue to expand, increasing amount of misinformation has been observed across multiple platforms (e.g., Twitter), posing risks to public interest (Chen et al., 2022). Therefore, fact-checking methods are proposed to prevent the spreading of
1Our implementation is publicly available at https://github.com/yueeeeeeee/RAFTS.
false information before it leads to severe consequences (Litou et al., 2017; Hassan et al., 2017; Shu et al., 2017). For example, online fact-checking services (e.g., Snopes2) employ professional factcheckers to identify instances of misinformation. Nevertheless, human fact-checking involves a significant amount of manual work, proving to be less efficient confronted with the vast volume of misinformation, particularly as it evolves and spreads online (Micallef et al., 2020; Nakov et al., 2021).
To perform fact-checking at scale, automated methods have emerged by leveraging large language models (LLMs) (Shu et al., 2022; Yang et al., 2022; Yue et al., 2023; Choi and Ferrara, 2024). For example, RARG proposes to train and align LLMs for generating faithful explanations upon detected misinformation (Yue et al., 2024). Despite their effectiveness, these methods typically require extensive training data and may demonstrate performance deterioration upon domain / concept shifts (Zhu et al., 2022; Nan et al., 2022; Gu et al., 2023; Shang et al., 2024a). Moreover, many models are unaware of external evidence / knowledge and must be frequently re-trained to incorporate up-to-date domain knowledge for accurate factverification (Izacard and Grave, 2021; Borgeaud et al., 2022; Yue et al., 2023).
As a solution, evidence-based fact-checking methods are proposed to collect evidence (e.g., documents, graphs etc.), followed by extracting relevant information and assessing the credibility of input claims through LLMs (Koloski et al., 2022; Kou et al., 2022b; Shang et al., 2022a; Wu et al., 2022b; Zhang and Gao, 2023; Wang and Shu, 2023; Liu et al., 2024). An example is FOLK, which leverages first-order-logic to construct sub-claims and perform question answering-based verification to generate predictions and explanations (Wang and Shu, 2023). Yet current approaches rely on the assumption that input claims can be decomposed
2https://www.snopes.com/
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into a series of predicates (i.e., sub-claims) through complex prompts. Moreover, they depend on the embedded knowledge within LLMs and / or blackbox APIs (e.g., SerpAPI3) to collect external information, leading to subpar performance with smaller LLMs or provided with unreliable evidence (Zhang and Gao, 2023; Wang and Shu, 2023).
Consequently, we consider a retrieval augmented generation (RAG) framework designed to extract relevant information from reliable documents (i.e., Wikipedia, scholarly articles etc.), where the extracted information can be used as supporting facts to assess the claim credibility through LLMs. That is, given the input statement, our first objective is to retrieve (and optionally re-rank) relevant documents among an extensive collection of documents from verifiable sources. Subsequently, we utilize the retrieved documents to fact-check the input claim, aiming to either confirm the input or uncover opposing information that identifies misinformation. Our framework is visually illustrated in Figure 1, where the RAG-based fact verification framework retrieves relevant documents, and then generates both prediction and explanation regarding the validity of the input statement.
To this end, we propose retrieval augmented fact verification through the synthesis of contrastive arguments (RAFTS), which effectively retrieves relevant documents and performs few-shot fact verification using pretrained LLMs. RAFTS is structured into three components: (1) demonstration retrieval, where informative in-context examples are collected to improve fact-checking performance; (2) document retrieval, in which we design a retrieve and re-rank pipeline to accurately identify relevant documents for input claims; and (3) few-shot fact verification through the synthesis of contrasting arguments. Unlike current approaches, RAFTS formulates supporting and opposing arguments derived from the facts within the
3https://www.serpapi.com/
collected documents. Combined the informative incontext examples, RAFTS demonstrates enhanced fact-checking performance and consistently generates high-quality explanations. To validate the effectiveness of RAFTS, we adopt multiple benchmark datasets and perform extensive experiments on both document retrieval and fact verification. Experiment results highlight the effectiveness of the proposed approach, where RAFTS can outperform state-of-the-art methods even with a significantly smaller LLM (e.g., Mistral 7B).
We summarize our contributions:
1. We propose a RAG-based framework, where relevant documents are retrieved from reliable sources to fact-check input claims.
2. We design RAFTS in three key components: demonstration retrieval, document retrieval and in-context prompting. RAFTS identifies informative examples and relevant documents, followed by synthesizing contrastive arguments for fine-grained fact-checking.
3. We show the effectiveness of RAFTS by experimenting on document retrieval and fact verification tasks. Both quantitative and qualitative results demonstrate that RAFTS can outperform state-of-the-art methods in fact verification and explanation generation.

heading: 2 Related Work

heading: 2.1 Large Language Models and Retrieval Augmented Generation
text: Recent advancements in large language models (LLMs) have shown significantly enhanced capabilities in language comprehension and generation (Raffel et al., 2020; Brown et al., 2020; Wei et al., 2021; Ouyang et al., 2022; Chowdhery et al., 2022; Touvron et al., 2023; OpenAI, 2023; Jiang et al., 2024). Due to the vast number of parameters and extensive quantity of pretraining corpora,
LLMs can embed global knowledge within their parameters, and thus achieve significant performance improvements across diverse applications (OpenAI, 2023; Penedo et al., 2023; Sun et al., 2023). However, LLMs often fail to capture fine-grained knowledge and frequently generate inaccurate or fabricated information (also known as hallucination) (Peng et al., 2023; Rawte et al., 2023). To access up-to-date knowledge without costly retraining, retrieval augmented generation (RAG) has been proposed to generate text based on collected documents from verifiable sources (Guu et al., 2020; Lewis et al., 2020; Izacard and Grave, 2021; Borgeaud et al., 2022; Izacard et al., 2022; Shi et al., 2023; Ram et al., 2023; Wang et al., 2023a). For example, Self-RAG can dynamically fetch external documents to generate contents through the usage of special tokens for retrieval and reflection (Asai et al., 2023). Nevertheless, current RAG methods remain under-explored for fact verification, particularly regarding accurate evidence retrieval and fine-grained classification (Wang and Shu, 2023; Zhang and Gao, 2023). As such, our work studies retrieval augmented fact verification, which gathers evidence from reliable sources and integrates contrasting opinions to achieve fine-grained fact verification.

heading: 2.2 Fact Verification and Misinformation Detection
text: Fact verification methods can generally be divided into two main categories: (1) content-based approaches, where machine learning models predict and reason over input contents (e.g., text) to identify misinformation (Yue et al., 2022; Jiang et al., 2022; Yue et al., 2023; Chen and Shu, 2023a; Liu et al., 2023; Mendes et al., 2023; Huang et al., 2024). Incorporating additional attributes / modalities such as image and propagation paths can further enhance fact verification performance (Shang et al., 2021; Santhosh et al., 2022; Shang et al., 2022b; Wu et al., 2022c; Zhou et al., 2023; Yao et al., 2023; Qu et al., 2024); (2) evidence-based approaches, which involve gathering external knowledge (e.g., knowledge graphs or document pieces) as evidence to validate input claims and identify false information (Kou et al., 2021, 2022a; Wu et al., 2022a; Yang et al., 2022; Shang et al., 2022c; Xu et al., 2022; Zhao et al., 2023; Chen et al., 2023; Wang and Shu, 2023; Yue et al., 2024; Shang et al., 2024b). For example, HiSS adopts hierarchical
step-by-step prompting with off-the-shelf LLMs and black-box question answering (QA) pipelines to perform few-shot fact verification (Zhang and Gao, 2023). However, state-of-the-art fact verification methods primarily concentrate on improving accuracy via sophisticated prompts and / or intrinsic knowledge of LLMs, causing performance degrade upon smaller LLMs or domain shifts (Wang and Shu, 2023; Pelrine et al., 2023; Chen and Shu, 2023b). Therefore, we concentrate on retrieval augmented fact verification by collecting relevant documents from reliable sources, enabling LLMs to augment their knowledge base for claim verification. Furthermore, we exploit in-context prompting by learning from demonstrations and synthesizing contrastive arguments, and thus significantly improves fact-checking performance.

heading: 3 Preliminary
text: We consider the following problem setup: given input claim x (with label y) and k-shot demonstrations {(xi, yi)}ki=1, we aim to: (1) retrieve a set of m documents {di}mi=1 that provide relevant information to be used as supporting evidence; and (2) generate label ŷ and explanation e based on the input x, k-shot examples {(xi, yi)}ki=1 and retrieved evidence {di}mi=1. For each input x, we leverage a pretrained embedding model fembed to adaptively retrieve demonstrations {(xi, yi)}ki=1, whereas a retrieval model is learnt to predict {di}mi=1 and provide relevant information from verifiable sources. Based on the retrieved examples and documents, the predicted ŷ should ideally match the ground truth label y. In addition, the generated explanation e should demonstrate desirable properties (e.g., factuality), see example in Figure 1. We elaborate our settings in the following. Input & Output: Given a dataset with train and test splits X train and X test, we denote the document retrieval pipeline as fretrieve and the LLMbased fact-checking model as fcheck. Formally, our framework consists of two sub-problems in information retrieval (i.e., evidence collection) and fact verification (i.e., prediction and explanation), with each of the problem defined below:
• Document Retrieval: Given input claim x, human annotated document d and a collection of n documents {di}ni=1 (with d ∈ {di}ni=1), our objective is to learn a retrieval model fretrieve that ranks the claim-document pair with the highest score (fretrieve(x, d) =
max{fretrieve(x, di)}ni=1). During training, input x and d can be used to learn fretrieve. In inference, we collect a subset of m documents {di}mi=1 for fact verification, where m ≪ n.
• Fact Verification: Subsequently, we leverage both input x and collected documents {di}mi=1 from the previous step and utilize fcheck to generate: (1) prediction ŷ on the input credibility; and (2) explanation e on the reasoning of the prediction. To perform in-context prompting, we incorporate k-shot examples {(xi, yi)}ki=1 from X train as input (xi ̸= x). In other words, ŷ, e = fcheck({(xi, yi)}ki=1, {di}mi=1, x)).
Learning: Our retrieval pipeline fretrieve is parameterized by θ. To learn fretrieve, we maximize the score of the sampled input-document pair (x, d). That is, we minimize the expected loss L over X train: minθ E(x,d)∼X train [L(θ, (x, d))]. Meanwhile, the fact-checking model fcheck (i.e., pretrained LLM) remains unchanged to minimize training expenses. To optimize fact-checking performance of fcheck, we employ a lightweight embedding model fembed to select informative incontext demonstrations {(xi, yi)}ki=1, we elaborate the details in Section 4.1.

heading: 4 Methodology

heading: 4.1 In-Context Demonstrations
text: Current LLM-based approaches for fact verification utilize sophisticated prompts to identify misinformation, but depend on carefully designed prompts and static in-context demonstrations (Wei et al., 2022; Zhang and Gao, 2023). Nevertheless, the classification criteria often vary from domain to domain, causing performance drops when identical prompts are applied across different contexts (as we show in Section 5). In addition, diverse and informative examples are found to be helpful for performance, in particular for smaller yet more efficient LLMs (Liu et al., 2021; Zhang et al., 2022; Levy et al., 2023; Li and Qiu, 2023). As such, we design a retrieval pipeline to select incontext demonstrations, thereby enhancing the factchecking performance and mitigating performance deterioration issues across domains.
We formulate the in-context learning (ICL) problem as follows. Provided with k-shot examples {(xi, yi)}ki=1, we prompt a pretrained LLM with them as demonstrations to generate the fact-
checking prediction ŷ given input x:
ŷ = argmax y
fcheck(y|{(xi, yi)}ki=1, x), (1)
with fcheck returning the output probabilities of the LLM. The prediction can be obtained by selecting the output with the highest probability conditioned on the provided in-context examples and input claim. In contrast to existing prompting methods, in RAFTS, LLM receives the task description via in-context examples. As a result, the performance of fact verification is highly sensitive to the selection of {(xi, yi)}ki=1. To this end, we design a simple and efficient example retrieval pipeline, which is designed to choose semantically similar examples from the training set to maximize the relevance and informativeness of demonstrations {(xi, yi)}ki=1 during in-context learning.
Specifically, we adopt a pretrained embedding model, denoted with fembed (kept frozen in our RAFTS framework). The objective of our retrieval pipeline is to identify a set of k examples {(xi, yi)}ki=1 for each claim x, with:
{(xi, yi)}ki=1 = topk({sim(fembed(x), fembed(xi))}|X train| i=1 ), (2) where topk returns k largest elements from the given set (i.e., claims with highest similarity to x), while sim represents the cosine similarity function (i.e., sim(a, b) = a·b∥a∥∥b∥ ). In essence, Equation (2) encodes the examples from the training set X train (only needs to be performed once), and then identifies the top-k nearest elements by computing the highest cosine similarity scores. Overall, our in-context example retrieval pipeline performs similarity-based filtering to select semantically relevant examples, and thus optimizes the prior distribution for in-context learning. We additionally apply similarity thresholding by establishing a minimum cosine similarity of 0.5, and set k = 10 as the maximum number of demonstrations. In our implementation, SimCSE-RoBERTa is employed as the embedding function fembed to encode input claims (Liu et al., 2019; Gao et al., 2021).

heading: 4.2 Document Retrieval
text: The majority of RAG and fact-checking methods utilize sparse retrieval algorithms, dense retrieval models or third-party APIs to collect relevant documents (Izacard and Grave, 2021; Izacard et al., 2022; Ram et al., 2023; Wang and Shu, 2023;
Zhang and Gao, 2023). While sparse retrieval methods are widely used, they often fall short in delivering optimal retrieval results for knowledgeintensive tasks like fact verification. On the other hand, dense retrieval methods suffer from efficiency issues in processing massive document collections and require extensive annotated data for optimal performance. These constraints render current retrieval approaches less effective for fact verification, where no / limited annotated claimdocument pairs are available for training.
Therefore, we propose a two-stage pipeline fretrieve in RAFTS that performs coarse-to-fine retrieval, which improves both computation efficiency and retrieval performance. Specifically, our pipeline includes: (1) sparse retrieval via BM25, which collects a subset {di}m̂i=1 from a large collection of documents; and (2) an dense retrieval model (denoted with θ) that re-ranks and refines the selection of retrieved documents. Based on x, the first step narrows down to a subset from a much larger collection {di}ni=1, while the learnable dense retriever further selects the m most relevant documents {di}mi=1 to verify input validity. Although BM25 may retrieve less relevant or even irrelevant elements, we note that with proper selection of m̂, the desired documents tend to be found within the retrieved set for most cases. In our implementation, we use m̂ = 20 and m = 5 to balance the document retrieval performance and efficiency.
Following the sparse BM25 retrieval, we elaborate the learning of our dense retrieval model.
To enhance re-ranking performance with limited annotated data, we exploit the BM25 scores as a coarse estimation of claim-document relevance. That is, we utilize the BM25 scores from the previous retrieval stage, combined with a limited collection of annotated examples, to train the dense retriever model. Specifically for claim-document pair (x, d), we sample l positive documents {dpi }li=1 and l negative documents {dni }li=1 based on the BM25 and inverse BM25 scores, which avoids introducing extensive noise in training. Using the sampled documents, we construct a ranking loss to expand the margin between document d and the highest ranked document from {dpi }li=1 (i.e., fden(x, d) − max({fden(x, dpi )}li=1)). In addition, we enhance the relevance between input-document pairs by imposing a penalty when the margin is below threshold τ . Furthermore, our training objective incorporates a contrastive term derived from InfoNCE (Chen et al., 2020; Yue et al., 2024), which improves the relevance estimation between inputdocument pairs by ‘pushing away’ negative documents. Overall, the optimization objective is:
E(x,d)∼X [ max(0,max({f(x, dpi )}li=1)− f(x, d) + τ)
− λ exp(f(x, d)) exp(f(x, d)) + ∑ 2l exp(f(x, di)) ],
(3) where ∑ 2l exp(f(x, di)) represents the exponential sum of both positive examples {dpi }li=1 and negative examples {dni }li=1. τ is the ranking margin threshold and λ is a scaling factor. For each pair of
x and d, the first term in Equation (3) becomes relevant when fden(x, d) does not exceed the highest ranked document (i.e., also known as hard negative) score by τ . Moreover, the contrastive term maximizes exponential score of the input-document pair in contrast to the sum of scores from the sampled documents. Hence, the dense retriever model learns to prioritize highly relevant documents while effectively filtering out those of less relevance to improve fact verification performance.

heading: 4.3 Fact Verification by Synthesizing Contrastive Arguments
text: To facilitate fact verification with LLMs, existing methods leverage intricate templates and techniques such as chain-of-thought (CoT), which decomposes input claims into sub-claims to verify (Wei et al., 2022; Wang and Shu, 2023). Yet when assessing the (sub)-claims, current approaches prompt LLMs to perform binary classification (i.e., true or false), and thus often fail to incorporate nuanced information for fine-grained fact-checking (Zhang and Gao, 2023; Pelrine et al., 2023). Moreover, the extended context created by retrieved demonstrations and documents can impair performance in LLMs with limited context windows or in smaller LLMs. Therefore, we propose a branching approach by generating and synthesizing contrastive arguments, in which we: (1) decompose the fact-checking task into generating supporting and refuting arguments upon input claim and retrieved documents; and (2) learn from informative in-context examples to synthesize the contrasting arguments, which incorporates adaptive prior knowledge and varying viewpoints.
Provided with claim and retrieved documents, our first sub-task involves creating two branches in parallel that generate independent yet varying arguments from two opposing perspectives. In particular, we leverage the text comprehension and summarization capabilities of LLMs and perform instruction prompting to extract relevant facts and generate supporting / refuting arguments. We adopt a simple task description and optimize it to obtain concise, yet accurate arguments within a few sentences. For input x and retrieved documents {di}mi=1, the generated supporting / refuting arguments are denoted with s and r. Therefore, for a specific example (x, y) ∼ X , we enrich the input to (s, r, x, y) by integrating both supporting and refuting arguments. Notably, if no pertinent evidence
is found to form the argument, LLMs are instructed to recognize the absence of evidence, as illustrated in branch 1 of Figure 2. Consequently, this allows us to guide LLMs to take both arguments into consideration, facilitating a comprehensive analysis on the claim and its credibility.
Moving to the argument synthesis and inference phase (i.e., in-context synthesis) of our factchecking framework, we aim to generate accurate prediction on the claim validity by leveraging incontext examples along with the contrasting arguments. Recall our in-context learning framework condition on the k-shot examples {(xi, yi)}ki=1, we also incorporate the generated arguments and reformulate our inference with:
ŷ = argmax y fcheck(y|{si, ri, xi, yi}ki=1, s, r, x), (4) where si, ri are the supporting and refuting arguments for the i-th demonstration. Note that the documents {di}mi=1 are implicitly included in the arguments and thus no longer used in the prompt. At this point, we adopt the following template for each example in the final prompt:
Claim: Claim Supporting argument: Supporting Arg Refuting argument: Refuting Arg Based on the claim, its supporting and refuting arguments, it is clear that among Classes, the claim should be classified as Label.
Here, Claim, Supporting Arg, Refuting Arg, Classes are populated with the input claim, supporting and refuting arguments and the set of all classes. For in-context examples, Label is filled with the respective example’s label, whereas for the target example, Label is left blank for prediction. Following the prediction, the explanation is generated in a similar fashion by integrating both arguments and prompting with instruction.

heading: 4.4 Summary of RAFTS
text: Overall, the proposed RAFTS has three components: (1) example retrieval; (2) document retrieval; and (3) in-context fact verification. The first two components are designed to collect relevant demonstrations and supporting documents that provide insightful context information. In the third component, we propose to generate contrasting arguments upon the retrieved documents, followed by incorporating these perspectives in inference to achieve
fine-grained fact verification. With informative incontext examples featuring contrastive arguments, RAFTS can perform well regardless of the LLM size. To demonstrate the efficacy of RAFTS, we perform extensive experiments on multiple fact verification datasets, revealing that RAFTS can surpass state-of-the-art fact-checking methods even with a significantly smaller LLM.

heading: 5 Experiments

heading: 5.1 Experiment Design
text: Document Retrieval. Our example retrieval model fembed uses the pretrained SimCSE-RoBERTa (Liu et al., 2019; Gao et al., 2021). The document retrieval model fretriever consists of BM25 and a dense retriever initialized with E5 (base) (Wang et al., 2022). We adopt MS MARCO and CheckCOVID dataset for document retrieval (Nguyen et al., 2016; Wang et al., 2023b). The adopted metrics are NDCG and Recall (i.e., N@k and R@k) with k ∈ [1, 3, 5]. For baselines, we adopt the sparse TFIDF and BM25 and dense models DPR and E5 (Karpukhin et al., 2020; Wang et al., 2022). Fact Verification. We adopt Mistral 7B and GPT-3.5 as our base LLM (Jiang et al., 2023; Ouyang et al., 2022). We adopt three datasets with varying granularity: LIAR (True / Mostlytrue / Half-true / Barely-true / False / Pants-fire)), RAWFC (True / Half-true / False), and ANTiVax (True / False) (Wang, 2017; Yang et al., 2022; Hayawi et al., 2022). For LIAR and RAWFC, we adopt Wikipedia as document sources and use the MS MARCO trained retriever. The document collection for ANTiVax is collected from CORD and LitCOVID, thus we use the Check-COVID trained retriever (Karpukhin et al., 2020; Wang et al., 2020; Chen et al., 2021). Our supervised baselines are dEFEND, SentHAN, SBERT-FC and CofCED (Shu et al., 2019; Ma et al., 2019; Kotonya and Toni, 2020; Yang et al., 2022). GPT-3.5-based methods include GPT-3.5, CoT, ReAct and HiSS (Brown
et al., 2020; Wei et al., 2022; Yao et al., 2022; Zhang and Gao, 2023). We adopt macro recall, precision and F1 scores to evaluate fact-checking performance. Automated evaluation is used for explanation quality, including politeness, factuality and claim-relevance following (He et al., 2023).

heading: 5.2 Document Retrieval
text: Our document retrieval results are reported in Table 1. In this table, rows represent retrieval methods and the columns represent different datasets / metrics. For top-1 scores, we use N@1 since top-1 NDCG and Recall scores are equivalent in this case. From the results we observe: (1) RAFTS retriever consistently outperforms baseline methods across all metrics, with an average performance improvement of 3.56% across metrics and datasets. (2) In contrast to sparse retrieval along, the additional dense retriever significantly improves the ranking performance. For example, RAFTS achieves 37.61% performance improvement in Recall@5 compared to BM25 on Check-COVID. (3) The performance gains through our retrieval pipeline are more significant on the Check-COVID dataset. For instance, the relative improvement of NDCG@5 shifts from 0.35% to 8.05% when moving from MS MARCO to Check-COVID. Overall, we find the proposed retrieval pipeline in RAFTS performs well in collecting relevant documents. In addition, the retrieval pipeline proves to be essential for specialized domains like healthcare (e.g., COVID), leading to notable performance improvements.

heading: 5.3 Fact Verification
text: We proceed to discuss our fact verification performance of RAFTS, with the results reported in Table 2. Similarly, methods are depicted in rows and datasets / metrics are represented in columns. The first group of baseline methods comprise supervised approaches (i.e. from dEFEND to CofCED), followed by methods built upon GPT-3.5 (i.e. from GPT-3.5 to HiSS), and the bottom row incorporate
RAFTS with Mistral 7B and GPT-3.5. We use P, R and F1 to abbreviate precision, recall and F1 scores4, and we observe: (1) Both RAFTS variants demonstrates superior fact-checking performance across all datasets. For example, RAFTS with Mistral 7B outperforms the best baseline method in F1 by 8.8%, while RAFTS with GPT3.5 achieves a significant 12.0% performance gain on F1. (2) RAFTS with GPT-3.5 delivers the best classification results overall. In particular, it leads in precision / recall on two of the three datasets and achieves the highest F1 for all datasets, averaging a 7.8% increase in F1 performance. (3) Notably, RAFTS w/ Mistral 7B backbone is superior than all baseline methods on F1 scores despite its significantly smaller size (7B) than GPT-3.5. This suggests that the proposed in-context synthesis can extract concise yet informative arguments and help LLMs generate accurate predictions on claim credibility. In summary, the RAFTS can outperform state-of-the-art fact verification methods by a substantial margin. Even when utilizing a notably smaller model (Mistral 7B), RAFTS consistently exhibits superior performance, highlighting its efficacy in fact verification.

heading: 5.4 Explanation Generation
text: Based on the fact verification results, the explanations for the prediction can be generated in a similar fashion. To evaluate explanation quality, we benchmark against GPT-3.5 and HiSS, as supervised and the rest LLM methods are not designed to generate fact-checking explanations. We report the explanation quality results in Table 3, with Po., Fa. and Rel. representing politeness, factuality and
4In our experiments, F1 score is favored as it balances the trade-off between precision and recall, thereby offering a more comprehensive performance measure for fact verification.
claim relevance. For RAFTS, we use (M) and (G) to denote the Mistral 7B and GPT-3.5 backbones. Our findings are: (1) both baselines and RAFTS perform well in generating explanations based on the fact-checking predictions, achieving average scores above 0.9 for both politeness and factuality. (2) GPT-3.5-based methods show similar performance regardless of prompting strategies. For instance, the average scores on ANTiVax across metrics are 0.898, 0.909 and 0.915 for GPT-3.5, HiSS and RAFTS (G). (3) Surprisingly, RAFTS with Mistral excels in explanation generation, achieving the highest politeness and factuality scores on all datasets, which may be attributed to the instructionfollowing capabilities of Mistral 7B. In sum, the explanation evaluation shows that RAFTS can consistently generate high-quality explanations regardless of the choice of the LLM.

heading: 6 Conclusion
text: In this paper, we propose RAFTS, a novel retrieval augmented fact verification framework. RAFTS
consists of three key components: (1) example retrieval, which provides informative in-context demonstrations; (2) document retrieval that collects relevant documents from verifiable sources; and (3) in-context prompting, where few-shot fact-checking is performed by considering both informative examples and nuanced information from contrastive arguments. As a result, RAFTS achieves fine-grained fact verification without the need for complex prompting techniques and largesize LLMs. Our experiment results on benchmark datasets highlight the superiority of RAFTS, which consistently outperforms state-of-the-art methods methods in both fact-checking performance and the quality of generated explanations.

heading: 7 Limitations
text: Despite introducing RAFTS for retrieval augmented fact verification, we have not discussed the setting in which the document retrieval domain significantly differs from the fact-checking domain (e.g., using Wikipedia documents to factcheck COVID misinformation), which can cause performance deterioration for domain-generalized applications. Furthermore, we have not examined the robustness and reliability of our example retrieval and document retrieval, which could unlock additional improvements for fact verification. Consequently, we plan to explore a more generalized and domain-adaptive solution for retrieval augmented fact verification as future work.

heading: Acknowledgement
text: This research is supported in part by the National Science Foundation under Grant No. IIS-2202481, CHE-2105032, IIS-2130263, CNS-2131622, CNS2140999. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

heading: Future Work
text: Consequently, we plan to explore a more generalized and domain-adaptive solution for retrieval augmented fact verification as future work