# Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative LLMs

arXiv: https://arxiv.org/abs/2412.09247 · HTML: https://mlsanity.com/paper/2412.09247/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias](https://mlsanity.com/paper/2404.00929/) (2024)
- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking](https://mlsanity.com/paper/2405.04685/) (2024)
- [Irony Detection in a Multilingual Context](https://mlsanity.com/paper/2002.02427/) (2020)
- [Augmenting emotion features in irony detection with Large language modeling](https://mlsanity.com/paper/2404.12291/) (2024)
- [Pitfalls of Conversational LLMs on News Debiasing](https://mlsanity.com/paper/2404.06488/) (2024)

## Built on by (most cited first, 3 of 3)

- [Historical Ink: Exploring Large Language Models for Irony Detection in 19th-Century Spanish](https://mlsanity.com/paper/2503.22585/) (2025)
- [SeLeRoSa: Sentence-Level Romanian Satire Detection Dataset](https://mlsanity.com/paper/2509.00893/) (2025)
- [Grounded Satirical Generation with RAG](https://mlsanity.com/paper/2605.10853/) (2026)

## Abstract (arXiv)

Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection performances. This study proposes a debiasing approach for satire detection, focusing on reducing biases in training data by utilizing generative large language models. The approach is evaluated in both cross-domain (irony detection) and cross-lingual (English) settings. Results show that the debiasing method enhances the robustness and generalizability of the models for satire and irony detection tasks in Turkish and English. However, its impact on causal language models, such as Llama-3.1, is limited. Additionally, this work curates and presents the Turkish Satirical News Dataset with detailed human annotations, with case studies on classification, debiasing, and explainability.