# FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering

arXiv: https://arxiv.org/abs/2305.04329 · HTML: https://mlsanity.com/paper/2305.04329/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 56 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://mlsanity.com/paper/2011.03088/) (2020)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge](https://mlsanity.com/paper/2109.01653/) (2021)
- [Paraphrasing with Large Language Models](https://mlsanity.com/paper/1911.09661/) (2019)
- [FaVIQ: FAct Verification from Information-seeking Questions](https://mlsanity.com/paper/2107.02153/) (2021)
- [False Information on Web and Social Media: A Survey](https://mlsanity.com/paper/1804.08559/) (2018)
- [Improving Paraphrase Detection with the Adversarial Paraphrasing Task](https://mlsanity.com/paper/2106.07691/) (2021)
- [Explainable Fact-checking through Question Answering](https://mlsanity.com/paper/2110.05369/) (2021)
- [Unsupervised Paraphrasing with Pretrained Language Models](https://mlsanity.com/paper/2010.12885/) (2020)
- [WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipedia](https://mlsanity.com/paper/2109.00835/) (2021)
- [Automatic Fact-Checking Using Context and Discourse Information](https://mlsanity.com/paper/1908.01328/) (2019)
- [Parameter-Efficient Abstractive Question Answering over Tables or Text](https://mlsanity.com/paper/2204.03357/) (2022)

## Built on by (most cited first, 24 of 24)

- [Claim Verification in the Age of Large Language Models: A Survey](https://mlsanity.com/paper/2408.14317/) (2024)
- [QuanTemp: A real-world open-domain benchmark for fact-checking numerical claims](https://mlsanity.com/paper/2403.17169/) (2024)
- [FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering](https://mlsanity.com/paper/2306.05523/) (2023)
- [Unveiling Attractor Cycles in Large Language Models: A Dynamical Systems View of Successive Paraphrasing](https://mlsanity.com/paper/2502.15208/) (2025)
- [CLAIMCHECK: How Grounded are LLM Critiques of Scientific Papers?](https://mlsanity.com/paper/2503.21717/) (2025)
- [EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification](https://mlsanity.com/paper/2310.14508/) (2023)
- [CHECKWHY: Causal Fact Verification via Argument Structure](https://mlsanity.com/paper/2408.10918/) (2024)
- [Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science](https://mlsanity.com/paper/2506.04410/) (2025)
- [BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking](https://mlsanity.com/paper/2502.16181/) (2025)
- [SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection](https://mlsanity.com/paper/2312.00292/) (2023)
- ["Image, Tell me your story!" Predicting the original meta-context of visual misinformation](https://mlsanity.com/paper/2408.09939/) (2024)
- [Assessing News Thumbnail Representativeness: Counterfactual text can enhance the cross-modal matching ability](https://mlsanity.com/paper/2402.11159/) (2024)
- [EHRCon: Dataset for Checking Consistency between Unstructured Notes and Structured Tables in Electronic Health Records](https://mlsanity.com/paper/2406.16341/) (2024)
- [Navigating the Noisy Crowd: Finding Key Information for Claim Verification](https://mlsanity.com/paper/2407.12425/) (2024)
- [TabVer: Tabular Fact Verification with Natural Logic](https://mlsanity.com/paper/2411.01093/) (2024)
- [STRIVE: Structured Reasoning for Self-Improvement in Claim Verification](https://mlsanity.com/paper/2502.11959/) (2025)
- [Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning](https://mlsanity.com/paper/2403.10281/) (2024)
- [Overview of Factify5WQA: Fact Verification through 5W Question-Answering](https://mlsanity.com/paper/2410.04236/) (2024)
- [TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking](https://mlsanity.com/paper/2410.15135/) (2024)
- [CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs](https://mlsanity.com/paper/2504.14905/) (2025)
- [DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images](https://mlsanity.com/paper/2509.21787/) (2025)
- [City Navigation in the Wild: Exploring Emergent Navigation from Web-Scale Knowledge in MLLMs](https://mlsanity.com/paper/2512.15933/) (2025)
- [Fact-checking based fake news detection: a review](https://mlsanity.com/paper/2401.01717/) (2024)
- [MAAT: Multi-phase Adapter-Aware Targeted Unlearning](https://mlsanity.com/paper/2605.30514/) (2026)

## Abstract (arXiv)

Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude claim and conclude whether its truthful or a mere masquerade. Popular fact-checking websites follow a common structure for fact categorization such as half true, half false, false, pants on fire, etc. Therefore, it is necessary to have an aspect-based (delineating which part(s) are true and which are false) explainable system that can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. To that end, we present a semi-automatically generated dataset called FACTIFY-5WQA, which consists of 391, 041 facts along with relevant 5W QAs - underscoring our major contribution to this paper. A semantic role labeling system has been utilized to locate 5Ws, which generates QA pairs for claims using a masked language model. Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field. Lastly, we propose a robust fact verification system that takes paraphrased claims and automatically validates them. The dataset and the baseline model are available at https: //github.com/ankuranii/acl-5W-QA