# Logical Fallacy Detection

arXiv: https://arxiv.org/abs/2202.13758 · HTML: https://mlsanity.com/paper/2202.13758/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 26 of 26)

- [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)
- [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)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://mlsanity.com/paper/2004.02984/) (2020)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://mlsanity.com/paper/2009.02696/) (2020)
- [The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants](https://mlsanity.com/paper/1708.01425/) (2017)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation](https://mlsanity.com/paper/1802.06613/) (2018)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection](https://mlsanity.com/paper/1910.09982/) (2019)
- [Prta: A System to Support the Analysis of Propaganda Techniques in the News](https://mlsanity.com/paper/2005.05854/) (2020)
- [How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://mlsanity.com/paper/2106.02359/) (2021)
- [Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health](https://mlsanity.com/paper/2109.13770/) (2021)

## Built on by (most cited first, 25 of 25)

- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [Missing Counter-Evidence Renders NLP Fact-Checking Unrealistic for Misinformation](https://mlsanity.com/paper/2210.13865/) (2022)
- [Multitask Instruction-based Prompting for Fallacy Recognition](https://mlsanity.com/paper/2301.09992/) (2023)
- [How susceptible are LLMs to Logical Fallacies?](https://mlsanity.com/paper/2308.09853/) (2023)
- [Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments](https://mlsanity.com/paper/2212.07425/) (2022)
- [Case-Based Reasoning with Language Models for Classification of Logical Fallacies](https://mlsanity.com/paper/2301.11879/) (2023)
- [Deductive Additivity for Planning of Natural Language Proofs](https://mlsanity.com/paper/2307.02472/) (2023)
- [SR-LLM: Rethinking the Structured Representation in Large Language Model](https://mlsanity.com/paper/2502.14352/) (2025)
- [Socrates or Smartypants: Testing Logic Reasoning Capabilities of Large Language Models with Logic Programming-based Test Oracles](https://mlsanity.com/paper/2504.12312/) (2025)
- [Modeling Appropriate Language in Argumentation](https://mlsanity.com/paper/2305.14935/) (2023)
- [Multi-Task Learning Improves Performance In Deep Argument Mining Models](https://mlsanity.com/paper/2307.01401/) (2023)
- [Detecting Fallacies in Climate Misinformation: A Technocognitive Approach to Identifying Misleading Argumentation](https://mlsanity.com/paper/2405.08254/) (2024)
- [Investigating Persuasion Techniques in Arabic: An Empirical Study Leveraging Large Language Models](https://mlsanity.com/paper/2405.12884/) (2024)
- [Generative Debunking of Climate Misinformation](https://mlsanity.com/paper/2407.05599/) (2024)
- [Do Large Language Models Show Biases in Causal Learning?](https://mlsanity.com/paper/2412.10509/) (2024)
- [Follow My Lead: Logical Fallacy Classification with Knowledge-Augmented LLMs](https://mlsanity.com/paper/2510.09970/) (2025)
- [Do Large Language Models Show Biases in Causal Learning? Insights from Contingency Judgment](https://mlsanity.com/paper/2510.13985/) (2025)
- [A dataset of rated conceptual arguments](https://mlsanity.com/paper/2607.27499/) (2026)
- [Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation](https://mlsanity.com/paper/2307.15341/) (2023)
- [Can a Hallucinating Model help in Reducing Human "Hallucination"?](https://mlsanity.com/paper/2405.00843/) (2024)
- [MisSynth: Improving MISSCI Logical Fallacies Classification with Synthetic Data](https://mlsanity.com/paper/2510.26345/) (2025)
- [VIGIL: An Extensible System for Real-Time Detection and Mitigation of Cognitive Bias Triggers](https://mlsanity.com/paper/2604.03261/) (2026)
- [Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs](https://mlsanity.com/paper/2604.27618/) (2026)
- [ForEx: A Formal Verification Framework for Explainable Reasoning in Logical Fallacy Detection and Annotation](https://mlsanity.com/paper/2606.21867/) (2026)
- [DeflectBench: A Benchmark for Evaluating Rhetorical Fallacy Generation in LLMs](https://mlsanity.com/paper/2608.26119/) (2026)

## Abstract (arXiv)

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate. We encourage future work to explore this task as (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://github.com/causalNLP/logical-fallacy