Challenge: Existing methods for fact-checking lack external sources and human-understandable explanations for decision-making . existing methods lack external knowledge sources and explanations .
Approach: They propose a framework that uses the Web as an external knowledge source to retrieve relevant evidence for claims and generates reasons based on the retrieved evidence for datasets lacking explanations.
Outcome: The proposed method improves the transparency and interpretability of fact-checking systems by providing human-understandable explanations for decision-making processes.

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Generating Fact Checking Explanations (2020.acl-main)

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Challenge: Existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims.
Approach: They propose to combine the generation of justifications for verdicts on claims with the multi-task model to optimize both objectives at the same time rather than training them separately.
Outcome: The proposed model improves the informativeness, coverage and overall quality of the generated explanations, rather than training them separately.
ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)

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Challenge: Despite the fact that many fact-checking tools lack granularity and explainability, they lack the ability to be useful in various contexts.
Approach: They propose a text validation framework that provides granular explanations for each claim and localizes the specific problematic content to reduce cognitive load.
Outcome: The proposed framework provides granular explanations for each claim prediction and localizes and educates users on the specific content.
Leveraging fine-tuned Large Language Models with LoRA for Effective Claim, Claimer, and Claim Object Detection (2024.eacl-long)

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Challenge: Existing work on identifying claims has focused on sentence level, neglecting supplementary attributes such as the claimer and claim object of the claim.
Approach: They propose a novel approach to detect claims using large language models in natural language understanding and text generation.
Outcome: The proposed approach transforms claim, claimer and claim object detection task into QA setting.
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models (2023.findings-emnlp)

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Challenge: Existing claims verification models rely on annotated data, which is expensive to create at a large scale.
Approach: They propose a model that can verify complex claims without annotated data . they leverage the in-context learning ability of Large Language Models to translate a claim into a First-Order-Logic clause .
Outcome: The proposed model outperforms baseline models on three datasets . it performs well on the datasets, and the results are published online.
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (D19-1)

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Challenge: Existing efforts to verify factual claims are limited by small datasets or artificially constructed datasets.
Approach: They propose to use the largest publicly available dataset of naturally occurring factual claims for automatic claim verification.
Outcome: The proposed model outperforms baseline models and evidence pages significantly.
A Survey on Automated Fact-Checking (2022.tacl-1)

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Challenge: Fact-checking is an essential task in journalism due to the speed with which information and misinformation can spread in the media ecosystem.
Approach: They propose to use natural language processing to automate fact-checking by identifying common concepts and defining definitions.
Outcome: The proposed method can predict the veracity of claims using natural language processing, machine learning, and databases.
Claim Verification in the Age of Large Language Models: A Survey (2026.acl-srw)

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Challenge: Recent election cycles have seen a large number of false information spread across social media and news platforms.
Approach: They propose a framework for automated claim verification using Large Language Models and Retrieval Augmented Generation.
Outcome: The proposed frameworks are based on large-scale models and new methods such as Retrieval Augmented Generation (RAG).
EuroVerdict: A Multilingual Dataset for Verdict Generation Against Misinformation (2025.findings-acl)

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Challenge: a global issue that shapes public discourse shapes opinion and decision-making . many multilingual work has focused on claim verification rather than generating explanatory verdicts .
Approach: They propose a multilingual dataset designed for verdict generation covering eight European languages.
Outcome: The EuroVerdict dataset covers claims, manual verdicts, and supporting evidence . it is compared with other datasets in eight European languages .
Benchmarking the Generation of Fact Checking Explanations (2023.tacl-1)

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Challenge: Automating fact-checking is a time-consuming task that cannot keep up with the ever-increasing amount of fake news produced daily.
Approach: They propose to automate the process of fact-checking by generating justifications from textual explanations of why a claim is classified as either true or false.
Outcome: The proposed approach improves summarization performance over unstructured knowledge and with two datasets with different styles and structures.
X-Fact: A New Benchmark Dataset for Multilingual Fact Checking (2021.acl-short)

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Challenge: Several fact-checking initiatives, such as PolitiFact, expend manual labor to investigate and determine the truthfulness of viral statements.
Approach: They propose a multilingual dataset for factual verification of naturally existing claims . they use a benchmark to evaluate the multilingual models .
Outcome: The proposed model achieves an F-score of around 40%, suggesting it is a challenging benchmark for multilingual fact-checking models.

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