| Challenge: | Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity. |
| Approach: | They propose to use instruction-based prompting to recognize 28 unique fallacies across datasets . they also analyze the effect of model size and prompt choice on model performance . |
| Outcome: | The proposed approach can recognize 28 unique fallacies across domains and genres. |
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| Challenge: | Existing work on fallacy recognition is still in its early stages, with limited datasets available. |
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| Challenge: | Prompting has reduced the data requirement by reusing the language model head and formatting the task input to match the pre-training objective. |
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| Challenge: | Existing fallacy classifiers lack sufficient labeled data for training, limiting their out-of-distribution (OOD) generalization abilities. |
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| Challenge: | Existing work on Goal-Oriented Scripts ignore usage context and personal preferences . proposed tasks are restrictive and rely on overly simplified assumptions . |
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| Challenge: | Existing approaches to prompt optimization are limited to learning multiple facets of a task from training examples. |
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Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation (2025.findings-naacl)
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| Challenge: | Recent large language models have demonstrated improved processing of complex language, but detecting logical fallacies remains a challenge. |
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Revisiting Automated Prompting: Are We Actually Doing Better? (2023.acl-short)
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Reframing Instructional Prompts to GPTk’s Language (2022.findings-acl)
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| Challenge: | Using reframing techniques, we find that instructional prompts are easier to follow for Language Models (LMs) |
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PromptNER: Prompt Locating and Typing for Named Entity Recognition (2023.acl-long)
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Yongliang Shen, Zeqi Tan, Shuhui Wu, Wenqi Zhang, Rongsheng Zhang, Yadong Xi, Weiming Lu, Yueting Zhuang
| Challenge: | Existing methods for prompt learning require a multi-round prompting manner and require elaborate templates. |
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