# Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech

arXiv: https://arxiv.org/abs/2209.04889 · HTML: https://mlsanity.com/paper/2209.04889/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 11 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Learning Explanatory Rules from Noisy Data](https://mlsanity.com/paper/1711.04574/) (2017)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)
- [Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge](https://mlsanity.com/paper/1901.07931/) (2019)
- [NUBIA: NeUral Based Interchangeability Assessor for Text Generation](https://mlsanity.com/paper/2004.14667/) (2020)
- [A Study of Automatic Metrics for the Evaluation of Natural Language Explanations](https://mlsanity.com/paper/2103.08545/) (2021)
- [Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web](https://mlsanity.com/paper/1610.03452/) (2016)
- [Investigating Memorization of Conspiracy Theories in Text Generation](https://mlsanity.com/paper/2101.00379/) (2021)
- [Do explanations increase the effectiveness of AI-crowd generated fake news warnings?](https://mlsanity.com/paper/2112.03450/) (2021)

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

- [Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models](https://mlsanity.com/paper/2311.09210/) (2023)
- [Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech](https://mlsanity.com/paper/2302.07736/) (2023)
- [Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://mlsanity.com/paper/2404.15676/) (2024)
- [Usable XAI: 10 Strategies Towards Exploiting Explainability in the LLM Era](https://mlsanity.com/paper/2403.08946/) (2024)
- [On Improving Summarization Factual Consistency from Natural Language Feedback](https://mlsanity.com/paper/2212.09968/) (2022)
- [Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI](https://mlsanity.com/paper/2309.12444/) (2023)
- [AI for social science and social science of AI: A Survey](https://mlsanity.com/paper/2401.11839/) (2024)
- [An Investigation of Large Language Models for Real-World Hate Speech Detection](https://mlsanity.com/paper/2401.03346/) (2024)
- [Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models](https://mlsanity.com/paper/2312.15099/) (2023)
- [ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?](https://mlsanity.com/paper/2403.17368/) (2024)
- [Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models](https://mlsanity.com/paper/2403.18957/) (2024)
- [Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech](https://mlsanity.com/paper/2406.03953/) (2024)
- [MemeIntel: Explainable Detection of Propagandistic and Hateful Memes](https://mlsanity.com/paper/2502.16612/) (2025)
- [Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models](https://mlsanity.com/paper/2410.15116/) (2024)
- [Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models](https://mlsanity.com/paper/2309.12940/) (2023)
- [OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA](https://mlsanity.com/paper/2510.06371/) (2025)
- [Graph-Guided Textual Explanation Generation Framework](https://mlsanity.com/paper/2412.12318/) (2024)
- [Beyond LLM-as-a-Judge: Deterministic Metrics for Multilingual Generative Text Evaluation](https://mlsanity.com/paper/2604.05083/) (2026)
- [Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification](https://mlsanity.com/paper/2506.04693/) (2025)
- [Chinese Cyberbullying Detection: Dataset, Method, and Validation](https://mlsanity.com/paper/2505.20654/) (2025)
- [Vulnerability of LLMs' Stated Beliefs? LLMs Belief Resistance Check Through Strategic Persuasive Conversation Interventions](https://mlsanity.com/paper/2601.13590/) (2026)
- [Chain-of-Experience for Continual LLM Improvement](https://mlsanity.com/paper/2608.18027/) (2026)
- [From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discourse](https://mlsanity.com/paper/2509.14712/) (2025)
- [KALE: Enhancing Knowledge Manipulation in Large Language Models via Knowledge-aware Learning](https://mlsanity.com/paper/2601.07430/) (2026)
- [Explain the Flag: Contextualizing Hate Speech Beyond Censorship](https://mlsanity.com/paper/2604.14970/) (2026)

## Abstract (arXiv)

Recent studies have exploited advanced generative language models to generate Natural Language Explanations (NLE) for why a certain text could be hateful. We propose the Chain of Explanation (CoE) Prompting method, using the heuristic words and target group, to generate high-quality NLE for implicit hate speech. We improved the BLUE score from 44.0 to 62.3 for NLE generation by providing accurate target information. We then evaluate the quality of generated NLE using various automatic metrics and human annotations of informativeness and clarity scores.