ClaimVer: Explainable Claim-Level Verification and Evidence Attribution of Text Through Knowledge Graphs (2024.findings-emnlp)
Copied to clipboard
Preetam Prabhu Srikar Dammu, Himanshu Naidu, Mouly Dewan, YoungMin Kim, Tanya Roosta, Aman Chadha, Chirag Shah
| 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. |
Similar Papers
MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing methods for verification of claims rely on textual evidence only or ignore the explainability. |
| Approach: | They propose a multi-modal reasoning model that integrates text and visual evidence for verification. |
| Outcome: | The proposed model achieves evidence retrieval, multi-modal claim verification, and explanation generation. |
Benchmarking the Generation of Fact Checking Explanations (2023.tacl-1)
Copied to clipboard
| 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. |
A Systematic Survey of Claim Verification: Corpora, Systems, and Case Studies (2025.findings-emnlp)
Copied to clipboard
| Challenge: | This survey analyses 198 studies published between January 2022 and March 2025 . |
| Approach: | This survey synthesizes recent advances in CV corpus creation and system design. |
| Outcome: | The results of this study are synthesized from 198 studies published between January 2022 and March 2025. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
Copied to clipboard
| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
Triple-R: Automatic Reasoning for Fact Verification Using Language Models (2024.lrec-main)
Copied to clipboard
| 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. |
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. |
Knowledge Graphs for Real-World Rumour Verification (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent advances in automated rumour verification have limited results in real-world scenarios. |
| Approach: | They propose to use Twitter responses to construct knowledge graphs based on the PHEME dataset to identify discrepancies between the evidence retrieved and PHE ME’s labels. |
| Outcome: | The proposed model outperforms the state-of-the-art on PHEME and has superior generisability when evaluated on a temporally distant rumour verification dataset. |
ProoFVer: Natural Logic Theorem Proving for Fact Verification (2022.tacl-1)
Copied to clipboard
| Challenge: | Recent fact verification systems rely on neural network classifiers for veracity prediction, which lack explainability. |
| Approach: | They propose a model that generates natural logic-based inferences as proofs using lexical mutations between spans in the claim and the evidence retrieved. |
| Outcome: | The proposed model has highest label accuracy and second best score in the FEVER leaderboard. |
Towards Effective Extraction and Evaluation of Factual Claims (2025.acl-long)
Copied to clipboard
| Challenge: | Lack of a standardized evaluation framework impedes assessment and comparison of claim extraction methods. |
| Approach: | They propose a framework for evaluating claim extraction in the context of fact-checking . they also introduce Claimify, an LLM-based claim extraction method . |
| Outcome: | The proposed evaluation framework outperforms existing methods in the evaluation of claim extraction methods. |
Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers (2025.findings-emnlp)
Copied to clipboard
| Challenge: | predicting the final label alone is insufficient and offers limited interpretability. |
| Approach: | They propose to reframe table–text alignment as an explanation task requiring models to identify the table cells essential for claim verification. |
| Outcome: | The proposed taxonomy improves claim verification performance and most LLMs fail to recover human-aligned rationales, suggesting that their predictions do not stem from faithful reasoning. |