# WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia

arXiv: https://arxiv.org/abs/2111.08543 · HTML: https://mlsanity.com/paper/2111.08543/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 58 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [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)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering](https://mlsanity.com/paper/1911.10470/) (2019)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Reasoning about Entailment with Neural Attention](https://mlsanity.com/paper/1509.06664/) (2015)
- [Natural Language Inference over Interaction Space](https://mlsanity.com/paper/1709.04348/) (2017)
- [Citation Needed: A Taxonomy and Algorithmic Assessment of Wikipedia's Verifiability](https://mlsanity.com/paper/1902.11116/) (2019)
- [WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipedia](https://mlsanity.com/paper/2109.00835/) (2021)
- [Wikipedia-based Semantic Interpretation for Natural Language Processing](https://mlsanity.com/paper/1401.5697/) (2014)
- [Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph](https://mlsanity.com/paper/2004.06842/) (2020)
- [Wiki-Reliability: A Large Scale Dataset for Content Reliability on Wikipedia](https://mlsanity.com/paper/2105.04117/) (2021)
- [Contradiction Detection for Rumorous Claims](https://mlsanity.com/paper/1611.02588/) (2016)

## Built on by (most cited first, 17 of 17)

- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia](https://mlsanity.com/paper/2406.13805/) (2024)
- [Self-Contradictory Reasoning Evaluation and Detection](https://mlsanity.com/paper/2311.09603/) (2023)
- [ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models](https://mlsanity.com/paper/2311.09182/) (2023)
- [MAGIC: A Multi-Hop and Graph-Based Benchmark for Inter-Context Conflicts in Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.21544/) (2025)
- [Contradiction Detection in RAG Systems: Evaluating LLMs as Context Validators for Improved Information Consistency](https://mlsanity.com/paper/2504.00180/) (2025)
- [How does Misinformation Affect Large Language Model Behaviors and Preferences?](https://mlsanity.com/paper/2505.21608/) (2025)
- [WINELL: Wikipedia Never-Ending Updating with LLM Agents](https://mlsanity.com/paper/2508.03728/) (2025)
- [Detecting Corpus-Level Knowledge Inconsistencies in Wikipedia with Large Language Models](https://mlsanity.com/paper/2509.23233/) (2025)
- [CORG: Generating Answers from Complex, Interrelated Contexts](https://mlsanity.com/paper/2505.00023/) (2025)
- [HealthContradict: Evaluating Biomedical Knowledge Conflicts in Language Models](https://mlsanity.com/paper/2512.02299/) (2025)
- [SegSub: Evaluating Robustness to Knowledge Conflicts and Hallucinations in Vision-Language Models](https://mlsanity.com/paper/2502.14908/) (2025)
- [Discrepancy Detection at the Data Level: Toward Consistent Multilingual Question Answering](https://mlsanity.com/paper/2510.11928/) (2025)
- [Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics](https://mlsanity.com/paper/2407.07038/) (2024)
- [Using contradictions improves question answering systems](https://mlsanity.com/paper/2211.05598/) (2022)
- [The SMeL Test: A simple benchmark for media literacy in language models](https://mlsanity.com/paper/2508.02074/) (2025)
- [On Finding Inconsistencies in Documents](https://mlsanity.com/paper/2512.18601/) (2025)

## Abstract (arXiv)

While Wikipedia has been utilized for fact-checking and claim verification to debunk misinformation and disinformation, it is essential to either improve article quality and rule out noisy articles. Self-contradiction is one of the low-quality article types in Wikipedia. In this work, we propose a task of detecting self-contradiction articles in Wikipedia. Based on the "self-contradictory" template, we create a novel dataset for the self-contradiction detection task. Conventional contradiction detection focuses on comparing pairs of sentences or claims, but self-contradiction detection needs to further reason the semantics of an article and simultaneously learn the contradiction-aware comparison from all pairs of sentences. Therefore, we present the first model, Pairwise Contradiction Neural Network (PCNN), to not only effectively identify self-contradiction articles, but also highlight the most contradiction pairs of contradiction sentences. The main idea of PCNN is two-fold. First, to mitigate the effect of data scarcity on self-contradiction articles, we pre-train the module of pairwise contradiction learning using SNLI and MNLI benchmarks. Second, we select top-K sentence pairs with the highest contradiction probability values and model their correlation to determine whether the corresponding article belongs to self-contradiction. Experiments conducted on the proposed WikiContradiction dataset exhibit that PCNN can generate promising performance and comprehensively highlight the sentence pairs the contradiction locates.