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Generative Large Language Models in Automated Fact-Checking: A
Survey
Ivan Vykopal1,2, Matúš Pikuliak2, Simon Ostermann3, Marián Šimko2
1Faculty of Information Technology, Brno University of Technology, Brno, Czech Republic
2Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia
{name.surname}@kinit.sk
3German Research Center for Artificial Intelligence (DFKI), Saarbrücken, Germany
simon.ostermann@dfki.de
Abstract
The dissemination of false information on on-
line platforms presents a serious societal chal-
lenge. While manual fact-checking remains
crucial, Large Language Models (LLMs) of-
fer promising opportunities to support fact-
checkers with their vast knowledge and ad-
vanced reasoning capabilities. This survey ex-
plores the application of generative LLMs in
fact-checking, highlighting various approaches
and techniques for prompting or fine-tuning
these models. By providing an overview of
existing methods and their limitations, the sur-
vey aims to enhance the understanding of how
LLMs can be used in fact-checking and to fa-
cilitate further progress in their integration into
the fact-checking process.
1 Introduction
The modern digital era has introduced numerous
challenges, including the widespread dissemina-
tion of false information, a problem exacerbated
by the rise of social media. Fact-checking is a key
strategy for combating false information (Vlachos
and Riedel,2014), but it is still largely carried out
manually by fact-checkers. As the number of fact-
checkers remains insufficient to keep pace with the
growing volume of misinformation (Aïmeur et al.,
2023), efforts are underway to develop automated
fact-checking systems. These systems leverage ar-
tificial intelligence and LLMs to reduce the work
of fact-checkers (Nakov et al.,2021a).
The application of LLMs in automated fact-
checking has demonstrated significant potential for
improving both the efficiency and accuracy of fact-
checking (Wang et al.,2023a). LLMs are trained on
large-scale datasets, and they leverage billions of
parameters to capture nuances and patterns of natu-
ral language. Moreover, generative LLMs, specifi-
cally designed for text generation, open new possi-
bilities for integrating LLMs into the verification
process by generating meaningful, natural text that
was not possible before and automating complex
reasoning tasks.
Previous surveys on fact-checking in NLP do-
main have primarily focused on the needs of fact-
checkers (Nakov et al.,2021a;Hrckova et al.,
2024), specific fact-checking tasks (Guo et al.,
2022b), datasets (Guo et al.,2022a) or traditional
and BERT-based methods (Thorne and Vlachos,
2018;Zeng et al.,2021). One recent survey ad-
dresses combating misinformation using LLMs,
illustrating both the opportunities and challenges
they present (Chen and Shu,2023b). However,
this study lacks a detailed exploration of the meth-
ods employed. This presents a gap for a more
thorough examination of generative LLMs’ role in
fact-checking.
Our main contribution is a comprehensive
overview of approaches and limitations in using
generative LLMs for automated fact-checking. We
review 69 papers, highlighting relevant method-
ologies and innovative prompting techniques for
researchers exploring LLM-aided information ver-
ification. We analyze fact-checking tasks, LLM
methods, common techniques and languages cov-
ered in surveyed papers. Additionally, we outline
future challenges and potential directions for ad-
vancing the use of generative LLMs in the verifica-
tion process.
2 Methodology
2.1 Selection
To compile a set of relevant articles, we conducted
a manual search of papers presented at ACL,EACL,
NAACL and EMNLP conferences. Additionally, we
performed a keyword search using Google Scholar
and ArXiv. We expanded this collection by incorpo-
rating papers cited in related works and those that
cited any article from our initial list. Furthermore,
we included papers from the CheckThat! Shared
tasks. In total, we gathered 69 papers that employ
1
arXiv:2407.02351v2 [cs.CL] 30 Oct 2024
generative LLMs for fact-checking. Additional de-
tails about our selection process are provided in
Appendix A.
2.2 Observed Aspects
In our survey, we categorized the papers based on
several key aspects. First, we investigated fact-
checking tasks addressed in each study (Section 3)
and the methods employed to incorporate LLMs
for tackling these tasks (Section 4). These methods
represent a high-level abstraction of the approaches
used, categorized based on the output type gen-
erated by LLMs. Another aspect we examined is
techniques (Section 5), which refer to the strategies,
such as prompting and fine-tuning, used to adapt
and apply LLMs for specific fact-checking tasks to
ensure they generate desired outputs. Given that
false information is a global issue, we also ana-
lyzed languages covered in each study (Section 7).
Furthermore, we classified what type of knowledge
base was used for evidence retrieval (see Table 5)
and whether LLMs utilized evidence for the fact-
verification task (see Table 6). Relevant studies for
each task are summarized in Tables 3to 6.
2.3 Scope of the Survey
This survey is intended for researchers with exper-
tise in NLP and a fundamental understanding of
methods across the field. We aim to present an
overview of techniques employed in fact-checking
using generative LLMs. While fact-checking has
evolved to cover multiple modalities, our study is
confined to textual and tabular data analysis. Un-
like surveys that provide detailed descriptions of
fact-checking datasets, our focus is on the methods,
techniques, and challenges of leveraging genera-
tive LLMs. Our aim is to focus on the verification
of the truthfulness of a given input using LLMs,
rather than on evaluating LLM outputs for halluci-
nations (Ji et al.,2023) or factuality (Wang et al.,
2023a,2024).
3 Fact-Checking Tasks
Fact-checking is the process of verifying the truth-
fulness of a given claim (Guo et al.,2022b). This
process involves several distinct tasks, each essen-
tial for determining the credibility of the informa-
tion. In this section, we sort identified tasks based
on the number of related papers and highlight the
most common methods used for each task.
3.1 Fact Verification & Fake News Detection
Fact verification and fake news detection are the
most prominent tasks when incorporating LLMs
into the fact-checking process, with 53 out of 69
surveyed papers aimed at these tasks. Fact verifica-
tion involves assessing the veracity of a given claim
or few sentences. In contrast, fake news detection
aims to check the trustworthiness of longer texts,
such as news articles (Li and Zhou,2020).
A key approach (44 out of 53 papers) in fact
verification is to use LLMs to classify claims into
several categories, which can range from two (true
or false) to three (supports, refutes, or not enough
information) or even more classes (Buchholz,2023;
Hoes et al.,2023a). In addition to classification,
explanation generation (22
×
) is commonly em-
ployed in conjunction with generative LLMs (Al-
thabiti et al.,2023;Russo et al.,2023b;Cekinel
and Karagoz,2024;Kao and Yen,2024).
3.2 Evidence Retrieval
Thirteen research papers have addressed the task
of evidence retrieval using LLMs. Evidence re-
trieval is about gathering essential information
from trusted sources to assess the veracity of claims.
The evidence can take various forms, such as text
or tables. This evidence serves as the knowledge
necessary for accurate fact-checking. The selection
of an appropriate knowledge source is crucial, as
Wikipedia and the open web exhibit several weak-
nesses. In contrast, scientific reports (Bhatia et al.,
2021) offer a more credible alternative.
The predominant approach for evidence retrieval
using LLMs is ranking (5 out of 13) (Jiang et al.,
2021;Pradeep et al.,2021,2020). Additionally,
alternative strategies have explored the capabilities
of LLMs to generate queries (1
×
) for web re-
trieval (Prieto-Chavana et al.,2023), and rationale
selection (5
×
) (Wang et al.,2023b;Evans et al.,
2023;Kamoi et al.,2023b), in which LLMs are
tasked with identifying relevant sentences from the
retrieved documents.
3.3 Claim Detection
Eleven of 69 papers addressed the claim detection
task, indicating a moderate emphasis. Claim detec-
tion concerns identifying claims containing verifi-
able information necessitating verification. These
claims, often termed check-worthy or verifiable
claims, can include statements that may be false,
misleading, or potentially harmful to society.
2
Structured Output
(Sec. 4.1)
Unstructured Output
(Sec. 4.2)
Synthetic Data Generation
(Sec. 4.3)
Methods
Input: Predict the veracity of the claim and
provide explanation. Claim: {STATEMENT}
Output: There is no evidence that the …
...
Input: Rate the truthfulness of the following
statement: “{STATEMENT}” on the range 0-1.
Output: 0.75
Regression
Input: Generate search query: {STATEMENT}
Output: oecd global gdp growth forecast
may 2021
Explanation Generation Query Generation
Input: Generate a claim that is not check-worthy.
Sample1: What is the most common ...
Sample2: The newest iPhone model will have ...
...
Input: Does the input contain a check-worthy
claim? Answer "Yes" or "No". {STATEMENT}
Output: Yes
Classification
...
Part/Entire Dataset
Figure 1: A taxonomy of methods for integrating generative LLMs in fact-checking, illustrating examples of model
inputs and outputs. The methods employed with generative LLMs are classified based on the output type.
Typically, claim detection is approached as a
binary classification (7 out of 11), as LLMs are pri-
marily employed as binary classifiers. Additionally,
several researchers have explored the generative ca-
pabilities of LLMs to generate claims (4
×
) (Chen
et al.,2022;Gangi Reddy et al.,2022), allowing
them to produce a set of verifiable claims based
on a given text, or generate questions (1
×
) (Chen
et al.,2022), aiming to formulate queries for ver-
ification. Leveraging these generative techniques
allows LLMs to extract key information and de-
compose complex statements into easily verifiable
components or questions.
3.4 Previously Fact-Checked Claims
Detection
The task of previously-checked claims detection
aims to reduce redundant work for fact-checkers.
Currently, it is the least explored area using LLMs,
with only five identified papers. However, it is
essential for fact-checkers to ascertain whether a
claim has been previously fact-checked, as mis-
leading claims propagate across various sources
and languages (Pikuliak et al.,2023b). Thus, the
goal of previously fact-checked claims detection is
to identify similar claims from a database that have
already undergone fact-checking.
In the context of previously fact-checked claims
detection, LLMs are commonly employed to rank
or filter retrieved fact-checks using the information
retriever systems (3
×
) (Shaar et al.,2020;Shlisel-
berg and Dori-Hacohen,2022;Neumann et al.,
2023). Another approach is to use LLMs to classify
the pairwise relationships between claims and re-
trieved fact-checks as textual entailment (2
×
)Choi
and Ferrara (2023,2024).
4 Taxonomy of Methods
In this section, we discuss the methods of using
LLMs to perform various fact-checking tasks. We
categorize methods based on their output type.
This taxonomy is illustrated in Figure 1: (1) Struc-
tured output is used when LLMs process samples
and generate answers in a predefined structure: a
class for a classification task, a number for a re-
gression task, ranking of inputs, etc. In this case,
LLMs often replace older specialized approaches.
(2) Unstructured output is used when LLMs pro-
cess samples and generate texts such as explana-
tions, summaries, etc. that are used in the fact-
checking process. (3) Synthetic data generation is
an approach when LLMs generate new samples for
our datasets. These datasets can then be used to
train other (often smaller and specialized) models.
4.1 Structured Output
Methods that produce structured output are the
most common in fact-checking, appearing in 59
out of 69 papers. These methods are advanta-
geous because they allow easier evaluation, offer
higher execution efficiency and easier fine-tuning
due to the availability of datasets with labeled tar-
gets. This makes them particularly beneficial for
fact-checking tasks. In this section, we further
categorize these methods into three output types:
classification, regression and ranking.
Classification. LLM-based classification utilizes
LLMs to predict categorical labels from a prede-
fined set, e.g., veracity labels. In this approach,
LLMs are tasked to select the most probable
class. Classification is commonly employed in fact-
checking (54 out of 69), as many tasks involve
categorizing claims, especially in claim detection
(yes/no) or fact verification (e.g., false, half true,
3
true). It is also effective in detecting previously
fact-checked claims, as an entailment classifica-
tion between a given statement and previously fact-
checked claims (Choi and Ferrara,2023,2024).
Regression. In regression (7
×
), LLMs generate
predicted scores across various scales (e.g., 0-1,
0-100). However, in fact-checking, regression is
often redefined as a classification problem due to
the absence of datasets with score targets (Li et al.,
2023b;Vergho et al.,2024;Setty,2024). In this
scenario, LLMs predict a score from the selected
range, which is converted into a classification based
on a given threshold, typically 50% for binary clas-
sification. This approach is beneficial for its ability
to optimize thresholds based on identified LLM
characteristics (Pelrine et al.,2023). Regression
is widely used in previously fact-checked claims
detection and evidence retrieval, where claims or
pieces of evidence are ranked by predicted scores.
It is also applied, although less frequently, in fact
verification (Guan et al.,2023;Jiang et al.,2023;
Evans et al.,2023), where the goal is to predict the
truthfulness score of a claim.
Ranking. Ranking (8
×
) involves sorting claims,
documents, or pieces of evidence based on their
relevance to a given query. While less common
in fact-checking than other methods, it can still
play a significant role. One approach we define as
classification ranking uses the logits of particular
tokens (e.g.,
true
) produced by the LLM (Pradeep
et al.,2021). In this case, the LLM predicts a
particular class, and the probability associated with
that class is then used for ranking.
Another approach involves rationale selection
(5
×
), where LLMs are directly instructed to rank
evidence from a prompt. In this method, LLMs
identify and select the most relevant parts of the
retrieved evidence or their own knowledge, such as
by selecting relevant sentences (Evans et al.,2023;
Tan et al.,2023) or relevant rationales (Wang et al.,
2023b).
4.2 Unstructured Output
Methods with unstructured outputs typically in-
volve generating continuous text, which poses
greater challenges in assessment. However, these
methods provide more detailed responses, often in-
cluding justifications, which enhance fact-checking
by offering insights into the LLM’s reasoning.
Since generative LLMs produce open-ended out-
puts rather than selecting from a set of labels, they
can address a broader range of fact-checking tasks.
We categorized these methods based on the out-
put type generated by LLMs into three categories:
(1) coherent, meaningful text consisting of mul-
tiple sentences; (2) single-sentence outputs; and
(3) outputs consisting of only a few keywords or
phrases.
Explanation Generation. Explanation genera-
tion (22
×
) is a method of generating textual expla-
nations that clarify the reasoning behind an LLM’s
output, particularly why a specific claim was cate-
gorized in a certain way, such as being classified as
false (Russo et al.,2023c). The generated output is
commonly meaningful, continuous text that offers
insights into the model’s decision-making process.
In addition to directly prompting LLMs to ex-
plain their decision, summarization can also be
used as a form of explanation generation, in
tasks like fact-verification (Russo et al.,2023b).
However, the limitation of summarization lies in its
tendency to omit crucial details from the retrieved
evidence, leading to summaries that may not fully
address misleading claims, nor be as persuasive as
traditional fact-checking articles.
Claim and Question Generation. The goal of
claim generation is usually to produce one sen-
tence that captures all key information for various
tasks, including claim detection,evidence retrieval
or fact-verification. The claim generation can also
be known as claim normalization (Sundriyal et al.,
2023), where the aim is to extract a single nor-
malized claim from a social media post. However,
challenges arise when multiple claims are present
in the input, complicating the creation of a single
normalized claim. Therefore, claim generation can
also produce a list of claims.
A part of claim generation is span detec-
tion (Gangi Reddy et al.,2022), which aims to
identify the exact boundaries of the claim within
the text. The advantage of span detection lies in its
ability to select the precise wording of the claim.
Claim generation and question generation share
a similar output format – a single sentence. How-
ever, in question generation, the output specifically
consists of a question, mostly used to verify a given
claim (Chen et al.,2022).
Query Generation. Another form of unstruc-
tured output is query generation (Prieto-Chavana
et al.,2023), where the output comprises several
words or short phrases primarily used for retrieving
4
evidence from external sources, such as search en-
gines. This method allows LLMs to extract essen-
tial parts of a claim, enhancing retrieval efficiency
and minimizing irrelevant results that may arise
from using the full claim.
Next hop prediction (Malon,2021) is a specific
type of query generation aimed at identifying and
generating the title with a sentence of the evidence
to be retrieved. This is especially important when
previously retrieved evidence introduces new enti-
ties that require further elaboration.
4.3 Synthetic Data Generation
In synthetic data generation, LLMs are employed
to create entire datasets or their parts. Unlike
the generation from Section 4.2, where LLMs are
used to generate results, synthetic data generation
creates data that are then used for training or fine-
tuning. This method proves valuable when datasets
are either unavailable or limited for specific tasks or
languages. In fact-checking, it can facilitate the cre-
ation of datasets containing disinformation (Chen
and Shu,2023a;Vykopal et al.,2024) or fake-
news (Jiang et al.,2023;Huang and Sun,2023).
Synthetic data generation can augment datasets
by filling gaps in underrepresented categories
or increasing variability. For instance, LLM can
generate debunked claims to extend datasets (Singh
et al.,2023), produce emotional misinformation
tweets (Russo et al.,2023a) or create counterparts
for claim-evidence pairs with contrasting veracity
and high word overlap (Zhang et al.,2024a).
5 Techniques
In this section, we analyze how different studies
applied techniques to guide LLMs in achieving
accurate outputs. We observed several popular
techniques to improve the performance of LLM,
which we categorized into three groups: prompt-
ing,fine-tuning and augmentation with external
knowledge. These techniques can overlap, with
some studies using combinations, such as linking
prompts with external resources. Papers were clas-
sified by the most prominent technique; for ex-
ample, if prompting was combined with external
knowledge, they were categorized under augmenta-
tion with external knowledge.
5.1 Prompting
Prompting is a set of techniques used to improve
the performance of LLMs by designing the instruc-
tions. This approach is versatile and particularly
effective in scenarios with limited data, such as
fact-checking, where datasets are often scarce and
labeled data is difficult to obtain.
However, prompting faces several limitations,
especially with complex tasks like in fact-checking.
For instance, detecting previously fact-checked
claims involves ranking, where prompting alone
may struggle to produce accurate results (Choi and
Ferrara,2024). Another limitation is the necessity
for clear instructions and contextual descriptions to
ensure accurate task execution. To reduce ambi-
guities, it is essential to define all relevant termi-
nology within the prompt, e.g., the attributes of
check-worthy claims. Therefore, prompt engineer-
ing is important, and LLMs can assist in prompt
creation (Cao et al.,2023).
Prompt structure and wording can influence
LLM performance. Techniques like role spec-
ification (Li and Zhai,2023) and JSON format-
ting (Ma et al.,2023) have been shown to enhance
performance. Role specification assigns the LLM
a defined role (e.g., fact-checker), improving task
comprehension, while JSON formatting structures
instructions and outputs in a consistent manner
(e.g.,
Check the claim: {claim}\nAnswer:
).
Additionally, output sequencing affect perfor-
mance (Vergho et al.,2024;Pelrine et al.,2023).
Presenting the explanation first encourages LLM
reasoning, leading to more accurate assessments,
whereas placing the final verdict first may cause
the LLM to focus on justifying the outcome rather
than producing a truthful explanation.
Fact-checking is challenging for prompting
LLMs, as their responses can be inaccurate.
Zeng and Gao (2023) addressed this by using mul-
tiple prompt variants, differing by a single word, to
represent different relationships to the claim (e.g.,
true, unclear, or false). This method helps ensure
consistency in the LLM’s output, as small changes
can affect predictions. Moreover, LLMs can be
prompted to rewrite claims in a neutral style, im-
proving evaluation reliability (Wu and Hooi,2023)
and resilience to adversarial attacks.
Prieto-Chavana et al. (2023) used prefixes like
Search query:
to guide LLMs in generating
search queries for evidence retrieval. Furthermore,
by combining query generation with rationale se-
lection, relevant documents are retrieved, and then
the most pertinent information is selected.
Few-Shot Prompting. Few-shot prompting
(23
×
) is a technique, where the model is provided
5
with multiple in-context examples, enhancing the
LLM’s understanding of the task. This approach
can mitigate the limitations of standard prompting,
where LLMs may struggle to fully comprehend the
task based only on the given instructions. Strong
performance, however, depends on the quality
of the examples employed. During inference,
LLMs can effectively learn the nuances of a task
from labelled examples (Brown et al.,2020). Pre-
vious studies have also explored how the number
of examples affects LLM performance (Cao et al.,
2023;Zeng and Gao,2023,2024).
In-context examples are particularly useful in
methods such as span detection (Gangi Reddy et al.,
2022) or claim generation (Kamoi et al.,2023b;Li
et al.,2023a). To reduce false positives, it is cru-
cial that the examples capture a broad range
of characteristics typical for the given task. De-
composing input text into sub-claims helps break
down complex statements into smaller, verifiable
components. However, during this process, LLM
may unintentionally reformulate claims, potentially
altering their meaning or veracity labels. By pro-
viding varied demonstrations, few-shot prompting
can aid LLMs in comprehending the variability and
defining characteristics of check-worthy claims.
Few-shot prompting extends beyond claim detec-
tion. It is also employed in evidence retrieval, such
as generating search queries (Li et al.,2023a) or
selecting relevant rationales (Wang et al.,2023b).
Chain-of-Thought. The Chain-of-Thought
(CoT) technique (Wei et al.,2024)leverages
LLMs’ reasoning abilities by incorporating
intermediate reasoning steps before generating
predictions. This technique can be employed in
both zero-shot (10
×
) (Cao et al.,2023;Chen and
Shu,2023a) or few-shot (6
×
) (Sawi´
nski et al.,
2023;Choi and Ferrara,2023;Pan et al.,2023a;
Wang and Shu,2023;Zhang and Gao,2023;Liu
et al.,2024a) settings. In few-shot CoT, authors
present examples that illustrate the reasoning
process involved, such as follow-up questions for
claim detection (Sawi´
nski et al.,2023) or examples
for normalized claim (Sundriyal et al.,2023).
5.2 Fine-Tuning
Fine-tuning (33
×
) is a method of adapting a pre-
trained model to a specific task or use case. This
technique is especially beneficial when models
lack the necessary capabilities to effectively ad-
dress a given task through prompting, particu-
larly in complex domains where LLMs may have
limited expertise. While fine-tuning can enhance
performance, it is typically a matter for smaller
LLMs due to the number of parameters. In contrast,
larger LLMs often possess enhanced capabilities
for tackling a wide range of tasks without addi-
tional fine-tuning, making the process less feasible
and often unnecessary.
Single-Task Fine-Tuning One approach to fine-
tuning involves fine-tuning on a single task (Agres-
tia et al.,2022;Petroni et al.,2021;Bhatia et al.,
2021;Hyben et al.,2023;Althabiti et al.,2023;
Russo et al.,2023b;Zeng and Zubiaga,2024),
which enhances the model’s specialization and
performance in that area. However, this method
may lead to the phenomenon of catastrophic forget-
ting, where LLMs lose the ability to perform other
tasks, even those within the same domain (Luo
et al.,2024). Consequently, addressing multi-
ple tasks requires fine-tuning a specific LLM
for each task. A strategy for single-task fine-
tuning involves classification ranking (Pradeep
et al.,2021). This approach has been extended
in previous works, e.g., by including named enti-
ties within the claim (Jiang et al.,2021), reranking
of previously retrieved documents (Pradeep et al.,
2021) or predicting the reliability of the retrieved
passage based on a given claim (Fernández-Pichel
et al.,2022). Classification ranking can be em-
ployed to rank evidence in evidence retrieval and
to extract pertinent sentences from retrieved evi-
dence (Pradeep et al.,2020).
Multi-Task Fine-Tuning. An alternative ap-
proach to single-task fine-tuning involves employ-
ing multiple tasks during training (Du et al.,2022).
This approach aims to create more generalizable
model by exploiting knowledge transfer across
tasks. While the primary goal is not to enhance
performance for a single task, each task can posi-
tively affect the others. Additionally, this approach
allows the LLM to be specialized for multiple tasks
simultaneously.
Prompt Style for Fine-Tuning. LLMs can
be fine-tuned using target labels and various
prompts. For fine-tuning encoder-decoder LLMs,
researchers commonly employ prompts formatted
as
"Claim: c Evidence: e Target:"
(Sarrouti
et al.,2021). In the case of decoder-only LLMs,
the input often consists only of the claim (Saw-
i´
nski et al.,2023). Given that contemporary LLMs
6
are frequently instruction-tuned, incorporating in-
structions along with the statement is a stan-
dard practice. Notably, decoder-only models gen-
erally outperform encoder-decoder models, espe-
cially when trained with instructional inputs.
Use-cases. In fact-checking, fine-tuning is
mostly utilized for ranking tasks, e.g., previ-
ously fact-checked claims detection and evidence
retrieval. It also extends to other applications,
including generating sub-questions (Chen et al.,
2022) and next-hop prediction (Malon,2021).
5.3 Augmentation with External Knowledge
Since information changes over time and LLMs
often lack up-to-date information, previous tech-
niques are frequently combined with external tools
and knowledge bases. This augmentation can in-
volve using previously retrieved evidence, web en-
gines, or external databases.
Evidence in Prompt. any approaches integrate
evidence from evidence retrieval into prompts
to fill LLM knowledge gaps. We refer to the
use of external knowledge as the open-book set-
ting (Schlichtkrull et al.,2023), while relying only
on internal knowledge is closed-book settings.
Given that multiple pieces of evidence are of-
ten available to verify claim’s veracity (Jiang
et al.,2021), two strategies are used: (1) verifying
the claim sentence by sentence and aggregating the
predicted labels for the final prediction; or (2) in-
cluding all relevant evidence within a single query.
The latter method tends to outperform the former,
as consolidating all necessary evidence into one
prompt leads to more accurate predictions. Be-
yond textual evidence, tabular data can also serve
as a source, and it is typically linearized before
being fed into LLMs (Zhang et al.,2024b).
Using External Tools. Besides incorporating re-
trieved evidence into prompts, LLMs often inter-
act with external tools, such as search engines
or knowledge bases (Zhang and Gao,2023;Yao
et al.,2023;Quelle and Bovet,2023). This tech-
nique provides several advantages, most notably
continuous access to up-to-date information, which
is essential for accurate fact-checking. However,
the effectiveness of these methods depends on the
careful selection of credible sources.
Retrieval-Augmented Generation. An effec-
tive strategy for mitigating hallucinations and
addressing outdated information in LLMs for
fact-checking applications is Retrieval Augmented
Generation (RAG). This method combines in-
formation retrieval with LLMs, enhancing their
factual accuracy by integrating credible external
sources, such as scientific databases and verified
fact-checking sites (Leippold et al.,2024). More-
over, RAG systems can be fine-tuned by jointly
training the retriever and the LLM, where the LLM
provides supervisory signals for training the re-
triever (Izacard et al.,2023;Zeng and Gao,2024).
By combining retrieval and generation, RAG en-
hances the effectiveness of the fact-checking pro-
cess against false information.
6 LLM Pipelines
In fact-checking, LLMs are commonly integrated
into complex orchestrated pipelines, where the out-
put of one step feeds into the next. Several studies
have proposed such pipelines, combining multiple
techniques from Section 5to aid fact-checkers.
A common approach is to decompose a claim
into verifiable sub-claims and predict the verac-
ity of each sub-claim.Zhang and Gao (2023) em-
ployed LLMs to break down claims by generating a
series of questions and answers. The LLM can then
rely on its internal knowledge or retrieve external
information to answer, assessing its confidence be-
fore making a prediction. Similarly, Wang and Shu
(2023) instructed LLMs to define predicates and
follow-up questions. After generating answers, the
LLM utilize a context of predicted questions and
their corresponding answers to assess each predi-
cate’s veracity, which is subsequently aggregated
into a final prediction and explanation.
Alternatively, claims can be assessed without
decomposition by directly tasking LLMs to gen-
erate questions and answers for veracity predic-
tion and to provide an overall assessment of the
claim (Chakraborty et al.,2023). The model then
decides whether the claim is supported, refuted or
lacks sufficient information. Another approach is
to convert claims into binary yes-or-no questions,
which are used to create interpretable logic clauses
for debunking misinformation (Liu et al.,2024a).
Beyond natural language, Pan et al. (2023b) in-
troduced a programming-based LLM for creat-
ing verification programs. This approach first em-
ploys a generative LLM to decompose claims into
questions, after which a programming LLM gener-
ates a reasoning program for verification. The re-
sulting code is executed with specific sub-task func-
7
tions to produce a final veracity prediction. This
framework serves as a baseline for fact-verification.
Since LLMs tend to produce hallucinated and
unfaithful explanations, generative LLMs can also
be used to enhance faithfulness (Kim et al.,2024).
This approach harnesses two LLM debaters, which
iteratively search for errors and flaws in the ex-
planation. Subsequently, all identified errors and
proposed corrections from both debaters are used
to adjust the final justification. This process is valu-
able in fact-checking, which requires convincing
and plausible explanations and fact-check articles,
as a human fact-checker normally would do.
7 Languages
We also examined the language coverage of each
work. Most papers focus on a single language,
primarily English (
56×
), with only one each for
Chinese and Arabic. Only 11 papers addressed
more than one language, typically pairing English
with another. Notably, only three explored ten or
more languages, with the largest experiments cov-
ering 114 languages (Setty,2024).
Prompt language. Most researchers use ap-
proaches designed for single-language settings in
multilingual contexts, typically providing instruc-
tions in English, while leaving the claims in their
original languages (Du et al.,2022;Agrestia et al.,
2022;Hyben et al.,2023;Cao et al.,2023;Huang
and Sun,2023;Pelrine et al.,2023;Li and Zhai,
2023). This allows for consistent prompts without
adaption. Alternatively, instructions can be in
the claim’s language, which requires predefined
instructions for each language and an accurate lan-
guage detector.
Multilingual training. An alternative approach
involves fine-tuning LLMs on multilingual data
to create models capable of handling multiple lan-
guages (Quelle and Bovet,2023). Another possible
strategy is cross-lingual transfer, where models
are trained on high-resource languages and then
evaluated in other languages.
8 Future Directions and Challenges
Knowledge-Augmented Strategies. Techniques
incorporating external knowledge into LLMs have
shown promise in addressing complex tasks in the
NLP domain, including fact-checking. Despite
their potential, these techniques remain underex-
plored in fact-checking, with only five out of 69
surveyed papers employing external sources. Fu-
ture research should focus on leveraging these tech-
niques, e.g., RAG, to enhance the efficiency and
accuracy of LLM-driven fact-checking.
Multilingual Fact-Checking. Given the global
nature of false information, leveraging LLMs for
fact-checking across languages is essential. De-
veloping effective multilingual fact-checking tech-
niques will enhance the ability of LLMs to detect
and address misinformation in diverse linguistic
contexts. One challenge includes the underex-
plored task of previously fact-checked claims de-
tection, which also involves cross-lingual claim-
matching – where input claims and fact-checks are
in different languages. This presents a valuable
opportunity for further research, highlighting the
potential of LLMs in multilingual fact-checking.
Fact-Checking in Real Time. Current methods
are mostly reactive, addressing claims after they
gain traction on social media. Generative LLMs
could enable real-time monitoring and analysis,
providing continuous updates on emerging infor-
mation. Integrating LLMs with real-time informa-
tion will help to identify and flag false information
as it arises, enhancing the timeliness and accuracy
of false claim detection and mitigating misinforma-
tion more effectively.
Interactive Fact-Checking. Interactive fact-
checking tools powered by generative LLMs rep-
resent a promising direction for users to verify
claims through dynamic dialogues. These sys-
tems could facilitate deeper engagement with fact-
checking, offering explanations and follow-up
questions. Future work should explore the develop-
ment of interactive fact-checking tools to empower
fact-checkers in false information mitigation.
9 Conclusion
The rapid advancement of generative LLMs has
sparked considerable interest in their potential ap-
plications within the fact-checking domain. This
study presents a systematic review of 69 research
papers, providing a detailed analysis of the various
methods that incorporate generative LLMs into the
information verification process. By examining the
techniques and approaches explored in the exist-
ing literature, this survey offers a comprehensive
overview of current methodologies and a founda-
tion for future research efforts to enhance and ex-
plore new frontiers in LLM-assisted fact-checking.
8
Limitations
Focus on Generative LLMs. This survey paper
focuses exclusively on approaches and techniques
employed in fact-checking that leverage generative
LLMs. While various types of language models
exist, such as encoder-only models, recent research
in NLP has increasingly centered on generative
LLMs due to their rapid advancements and ver-
satility across a wide range of tasks. Although
other model architectures could contribute to fact-
checking, this paper emphasizes generative LLMs
due to their growing prominence and potential in
this domain.
LLMs for Fact-Checking. Using LLMs in fact-
checking intersects with several related domains,
including hallucination, LLM factuality or truth
discovery. These domains address the issue of false
information from various perspectives. However,
most research on hallucination and LLM factuality
aims to evaluate the model’s outputs and determine
whether there are discrepancies between generated
answers and real-world facts. In contrast, this sur-
vey focuses on approaches and techniques for veri-
fying information rather than evaluating LLM out-
puts. Additionally, we include methods for improv-
ing the faithfulness of LLM-generated explanations
to enhance the reliability of fact-checking.
Textual Information Only. False information
extends beyond text and can involve various modal-
ities, such as images, videos, and audio. However,
this survey is limited to the problem of verifying
textual information. We excluded studies focused
on multimodal approaches, thereby narrowing our
scope to fact-checking methods that involve text
processing only.
Acknowledgements
This research was partially supported by DisAI - Im-
proving scientific excellence and creativity in com-
bating disinformation with artificial intelligence
and language technologies, a project funded by
Horizon Europe under GA No.101079164, by the
Central European Digital Media Observatory 2.0
(CEDMO 2.0), a project funded by the European
Union under the Contract No. 101158609, and
by the MIMEDIS, a project funded by the Slovak
Research and Development Agency under GA No.
APVV-21-0114.
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Keywords
fact-checking
check-worthy claim detection
claim detection
previously fact-checked claims detection
previously fact-checked claims retrieval
claim-matching
evidence retrieval
factuality prediction
claim verification
fact verification
fake-news detection
justification prediction
explanation generation
fact-checking dataset
disinformation detection
misinformation detection
rumour verification
rumour detection
Table 1: A list of keywords used when searching for
research papers.
A Methodology
To search for research papers, we employed mostly
search on ACL Anthology
1
, ArXiv and Google
Scholar. Since the fact-checking domain is still
evolving and there are multiple names for the same
fact-checking tasks, we used task names from pre-
vious surveys on fact-checking. Based on fact-
checking tasks, we defined a list of keywords for
the search that are shown in Table 1. The search
for articles was completed in February 2024. After
filtering out the papers that are not directly related
to fact-checking, are focused on fact-checking the
LLM’s output or do not use generative LLMs for
fact-checking, we identified a list of 69 articles.
B Datasets & Languages
After the rise of social media, the necessity for the
development of datasets aimed at verifying infor-
mation began to emerge. The first existing datasets
started to appear in early 2010 and their availability
increased over time. Many of these datasets have
been involved in experiments using LLMs in fact-
checking. Several of them come from Conference
and Labs of the Evaluation Forum (CLEF) and
specifically CheckThat! Lab focused on various
tasks related to information verification, political
bias of news articles, and anti-social behaviour,
such as hate speech or propaganda detection.
In addition, multiple datasets focused the-
matically on different topics, e.g. COVID-19,
the climate crisis, or political claims. Several
1https://github.com/acl-org/acl- anthology/
16
datasets were developed from data collected from
fact-checking organizations, for example, Multi-
Claim (Pikuliak et al.,2023b) or LIAR (Wang,
2017). Furthermore, multiple authors engaged their
own data collection resources, such as PolitiFact
2
or Google Fact Check Tools
3
, which collects veri-
fied claims from the Internet based on the Claim-
Review schema4.
A key attribute for identifying false claims in
cross-lingual settings is the existence of multilin-
gual datasets, comprising multiple languages or
a combination of language-specific datasets. Pre-
viously, most datasets have focused on one or a
few languages, specifically, English and Arabic are
the most represented languages. Recently, there
have been efforts to create multilingual datasets
that include a broader range of languages, includ-
ing e.g. MultiClaim (Pikuliak et al.,2023b) or
X-Fact (Gupta and Srikumar,2021). A list of all
identified datasets employed for the LLM experi-
ments is shown in Table 2.
C Fact-Checking Benchmarks
Several benchmarks have been developed to assess
the performance of LLMs in fact-checking tasks.
For example, Bang et al. (2023) focused on as-
sessing ChatGPT’s reasoning, hallucination, and
interactivity capabilities across eight tasks, includ-
ing misinformation detection. Another benchmark,
TRUS T LLM, scrutinized the trustworthiness and
safety features of 30 LLMs (Sun et al.,2024).
D Model Analysis
Various researchers have focused on different types
of LLMs within the reviewed papers. In con-
trast, these could be divided into two main cate-
gories: Encoder-Decoder (Seq2Seq) and Decoder-
only. Although the first Seq2Seq models already
emerged in 2019, their first applications in fact-
checking were seen in 2021, when the problem of
automated fact-checking began to receive increas-
ing attention. The most commonly used Seq2Seq
models include the T5 model, while we also en-
counter the T0 (Sanh et al.,2022), Flan-T5 (Chung
et al.,2022), or mT5 (Xue et al.,2021).
In contrast, the second category includes
Decoder-only LLMs, which we further divided
2https://www.politifact.com/
3https://toolbox.google.com/factcheck/explorer
4https://schema.org/ClaimReview
GPT-3.5 T5 GPT-4 GPT-3
Davinci Llama2 Flan-T5
Model
0
5
10
15
20
25
30
35
Number of occurances in papers
35
21
17
13 12
8
Figure 2: The occurrence frequency of the models in
the reviewed papers. We only present models used in
more than five papers.
into GPT-base,LLaMA-based and Other models,
similarly to Minaee et al. (2024).
1.
The GPT Family. Generative Pre-trained
Transformers (GPT) is a family of LLMs
developed by OpenAI and includes mostly
closed-source models, accessible by paid API.
This category includes all GPT models (e.g.
GPT-3, GPT-3.5, or GPT-4) or CODEX. In
addition to these closed-source models, there
are also several open-sourced models, such as
the GPT-Neo model.
2.
The LLaMA Family. The LLaMA family
is another collection of LLMs based on the
LLaMA or Llama2 foundation models devel-
oped by Meta. This category also includes
other models derived from LLaMA models,
such as Alpaca, Vicuna, or even Mistral.
3.
Other models. The last category consists
of models that do not fall under the GPT or
LLaMA family. Within this category, vari-
ous models have gained the attention of re-
searchers. Examples include the multilingual
BLOOM or the OPT, PaLM, and PaLM2 mod-
els. Along with these models, several authors
also investigated Atlas, a retrieval-augmented
language model.
Figure 2depicts LLMs employed in more than
five papers, accompanied by the respective count
of papers each model utilized. A total of 33 vari-
ous LLMs were employed in the collection of 69
papers.
17
Name
Claim detection
Previously fact-checked
claims detection
Evidence
retrieval
Fact
verification
# Lang. Size Citation
ArCOV19-Rumors ✓1 138 Haouari et al. (2021)
ArFactEx ✓ ✓ 1 100 Althabiti et al. (2024)
AveriTec ✓1 5k Schlichtkrull et al. (2023)
BoolQ-FV / AmbiFC ✓1 11k Glockner et al. (2023)
ChatGPT-FC ✓1 22k Li et al. (2023b)
Check-COVID ✓ ✓ 1 2k Wang et al. (2023b)
CHEF ✓1 10k Hu et al. (2022)
ClaimBuster ✓1 24k Arslan et al. (2020)
ClaimDecomp ✓1 1k Chen et al. (2022)
CLAN ✓1 6k Sundriyal et al. (2023)
CLEF-2020 ✓✓✓✓ 3 13K Barrón-Cedeno et al. (2020)
CLEF-2021 ✓ ✓ ✓ 5 22k Nakov et al. (2021b)
CLEF-2022 ✓ ✓ ✓ 7 36k Nakov et al. (2022)
CLEF-2023 ✓3 63k Alam et al. (2023)
CLIMATE-FEVER ✓1 2k Diggelmann et al. (2021)
Climate Feedback ✓1 N/A -
CoAID 1 5k Cui and Lee (2020)
Constraint ✓1 11k Patwa et al. (2021)
COVID-19 Scientific ✓1 142 Lee et al. (2020)
COVID-Fact ✓ ✓ 1 4k Saakyan et al. (2021)
Data Common ✓ ✓ X N/A -
e-FEVER ✓1 68k Stammbach and Ash (2020)
EnvClaims ✓1 29k Stammbach et al. (2023)
ExClaim ✓1 4k Gurrapu et al. (2022)
EX-FEVER ✓1 61k Ma et al. (2024)
FactEX ✓1 12k Althabiti et al. (2023)
FakeNewsNet ✓1 23k Shu et al. (2019)
FEVER ✓ ✓ 1 185k Thorne et al. (2018)
FEVEROUS ✓ ✓ 1 87k Aly et al. (2021)
FM2 ✓1 13k Eisenschlos et al. (2021)
FullFact ✓ ✓ 1 N/A -
GermEval 2021 ✓1 1k Risch et al. (2021)
Global-LIAR ✓1 600 Mirza et al. (2024)
HealthVer ✓ ✓ 1 14k Sarrouti et al. (2021)
HoVer ✓ ✓ 1 26k Jiang et al. (2020)
Labeled Unreliable News ✓1 74k Rashkin et al. (2017)
LESA-2021 ✓1 379k Gupta et al. (2021)
LIAR ✓1 13k Wang (2017)
LIAR++ ✓1 6k Russo et al. (2023b)
LIAR-NEW ✓2 2k Pelrine et al. (2023)
LLMFake 1 100 Chen and Shu (2023a)
Monant Medical Misinformation Dataset ✓ ✓ 1 51k Srba et al. (2022)
MS-MARCO ✓1 1011k Bajaj et al. (2018)
MSVEC ✓ ✓ 1 200 Evans et al. (2023)
MultiClaim ✓ ✓ ✓ 27/39 28k Pikuliak et al. (2023a)
NewsClaims ✓1 889 Gangi Reddy et al. (2022)
PolitiFact ✓ ✓ 1 N/A -
PolitiHop ✓1 500 Ostrowski et al. (2021)
PubHealth ✓1 12k Kotonya and Toni (2020)
RAWFC ✓1 2k Yang et al. (2022)
SciFact ✓ ✓ 1 1k Wadden et al. (2020)
Snopes ✓ ✓ 1 N/A -
Symmetric ✓1 1k Schuster et al. (2019)
TabFact ✓1 18k Chen et al. (2020)
TREC 2020 Health Misinfo. ✓