Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech
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.
Code (0)
등록된 구현이 없습니다.
Tasks
InformativenessText GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
From XAI to Stories: A Factorial Study of LLM-Generated Explanation Quality
Explainable AI (XAI) methods like SHAP and LIME produce numerical feature attributions that remain inaccessible to non expert users. Prior work has shown that Large Language Models (LLMs) can transform these outputs into…
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
Chain-of-thought (CoT) prompting enables large language models (LLMs) to solve complex reasoning tasks by generating an explanation before the final prediction. Despite it's promising ability, a critical downside of CoT …
Analogy-Driven Financial Chain-of-Thought (AD-FCoT): A Prompting Approach for Financial Sentiment Analysis
Financial news sentiment analysis is crucial for anticipating market movements. With the rise of AI techniques such as Large Language Models (LLMs), which demonstrate strong text understanding capabilities, there has bee…
Sentiment AnalysisLLM-Generated Explanations Do Not Suffice for Ultra-Strong Machine Learning
Ultra Strong Machine Learning (USML) refers to symbolic learning systems that not only improve their own performance but can also teach their acquired knowledge to quantifiably improve human performance. We introduce LEN…
Program SynthesisActive LearningAssertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations
Human educators possess an intrinsic ability to anticipate and seek educational explanations from students, which drives them to pose thought-provoking questions when students cannot articulate these explanations indepen…
Explanation GenerationFew-Shot Learning