Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-aware Prompt Formulation
Authors: Jiwon Jeong, Hyeju Jang, Hogun Park


Reviewer paper summaries: This paper presents an approach to improve the detection and classification of logical fallacies using LLMs such as GPT-4. While LLMs have shown advancements in processing complex language, accurately detecting logical fallacies remains a challenge. Authors propose a prompt formulation approach that enriches input text with counterarguments, explanations, and goals, enhancing both zero-shot and fine-tuned models. Method of this paper involves a two-step prompt formulation process that leverages implicit contextual information. Inspired by previous work, this paper incorporates three key types of implicit information. By augmenting the input prompt with this contextual information and using a prompt ranking method, they aim to provide additional context for identifying logical fallacies. Contributions of proposed method in this paper include a structured prompt design that integrates implicit contextual information and a confidence-based prompt ranking system. Extensive evaluations across multiple datasets demonstrate the effectiveness of the approach in various genres and domains.
Summary of strengths: 1.This paper introduces an approach to improving logical fallacy detection using LLMs. By incorporating counterarguments, explanations, and goals into the prompt formulation, they enhance the contextual understanding of the models, enabling more accurate detection and classification of logical fallacies.
2.This paper uses three key types of implicit contextual information to provide additional context for identifying logical fallacies.
3.They propose a confidence-based prompt ranking system, which ranks the generated queries based on their confidence scores.
4.This paper demonstrates the effectiveness of the proposed approach through extensive evaluations across diverse datasets featuring different types of logical fallacies, which contributes to the advancement of logical fallacy detection in natural language processing.
Summary of weaknesses: 1.The results are expected to be tested on more diverse and complex datasets, or you can create your own dataset according to datasets you have collected.
2.This paper focuses primarily on the technical aspects of the approach and may lack some sufficient discussion on the practical implementation of the proposed method.
3.When using formulas, you need to pay attention to linguistic norms: formulas are part of a sentence. Your formula is followed by a ",", but the first letter of your next sentence is capitalized, such as "For instance" in Section 3.
4.You might be able to resize the text and numbers in Figures 4 and 5 to make them look more appropriately sized.
5.The proposed method requires manual adjustment of the prompt.
Comments, suggestions and typos: 1.When using formulas, you need to pay attention to linguistic norms: formulas are part of a sentence. Your formula is followed by a ",", but the first letter of your next sentence is capitalized, such as "For instance" in Section 3.
2.You might be able to resize the text and numbers in Figures 4 and 5 to make them look more appropriately sized.
Overall assessment scores: 2.5