| Challenge: | Fact-checking real-world claims often requires collecting multiple pieces of evidence and complex multi-step reasoning. |
| Approach: | They propose a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions. |
| Outcome: | The proposed model outperforms seven baselines on two fact-checking datasets and has explicit output programs that benefit human debugging. |
Similar Papers
HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking (2024.lrec-main)
Copied to clipboard
| Challenge: | determining the trustworthiness of online medical content is challenging in the digital age . fact-checking is an approach to assess the veracity of factual claims . a new dataset is presented to help advance automated fact- checking . |
| Approach: | They propose a dataset that assesses the veracity of factual claims using evidence from credible sources. |
| Outcome: | The proposed dataset can be used for automated fact-checking tasks. |
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims (D19-1)
Copied to clipboard
Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, Jakob Grue Simonsen
| 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. |
Explainable Claim Verification via Knowledge-Grounded Reasoning with Large Language Models (2023.findings-emnlp)
Copied to clipboard
| 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. |
Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly being used to understand how scientific research evolves, drawing growing interest from the research community. |
| Approach: | They propose a scientific fact-checking dataset, SCINLP, tailored to the NLP domain that verifies the veracity of scientific research questions across varying rationale contexts. |
| Outcome: | The proposed framework examines scientific claims and research focus from a curated collection of influential and reputable NLP papers published between 2000 and 2024. |
Automated Fact Checking: Task Formulations, Methods and Future Directions (C18-1)
Copied to clipboard
| Challenge: | Recent research on fact checking has focused on misinformation . however, relevant papers and articles have been published in research communities that are unaware of each other and use inconsistent terminology. |
| Approach: | They propose avenues for future NLP research on automated fact checking . they highlight the use of evidence as an important distinguishing factor . |
| Outcome: | The proposed methods unify the task formulations and methodologies across papers and authors. |
Task-Oriented Automatic Fact-Checking with Frame-Semantics (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing work on automatic fact-checking relies on unstructured data and large language models to produce fact- check verdicts and explanations. |
| Approach: | They propose a new paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact- checking them. |
| Outcome: | The proposed paradigm improves evidence retrieval and explainability for fact-checking by leveraging frame semantics. |
Unknown Claims: Generation of Fact-Checking Training Examples from Unstructured and Structured Data (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for fact-checking are labor-intensive and time-consuming. |
| Approach: | They propose a framework that generates training instances for FC systems automatically using textual and tabular content. |
| Outcome: | The proposed framework generates training instances for FC systems using textual and tabular content. |
Complex Claim Verification with Evidence Retrieved in the Wild (2024.naacl-long)
Copied to clipboard
| Challenge: | Prior work makes simplifying assumptions in retrieval that depart from real-world use cases: no access to evidence, access to curated evidence, or access to published evidence after a claim was made. |
| Approach: | They propose a pipeline to check claims using raw evidence from the web . they restrict their retriever to only search documents available prior to the claim's making . |
| Outcome: | The proposed method is based on a political claim dataset and shows that the evidence summary produced by the system is reliable and relevant to answering key questions. |
Generating Literal and Implied Subquestions to Fact-check Complex Claims (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing fact-checking systems are not reliable because it is unclear which parts of a claim are true and which are not. |
| Approach: | They propose to decompose a political claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. |
| Outcome: | The proposed models can decompose a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. |
Generating Fact Checking Explanations (2020.acl-main)
Copied to clipboard
| 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. |