Pattern-based Logical Fallacy Detection using Decoder-Only Large Language Models

ACL ARR 2025 July Submission1332 Authors

Abstract: In today's fast-paced information era, logical fallacies, defined as defective patterns of reasoning, inevitably contribute to the growth of information disorder. However, often fallacies appear in nuanced forms that complicate automated identification. In this study, we investigate whether the logical structure of arguments proves beneficial for fallacy detection. To address the inherent variability of logical fallacies, we develop an experimental framework that extracts logical patterns from sentences via Large Language Models (LLMs) from the LOGIC dataset. We evaluate the impact of these patterns across different LLMs and experimental zero- and one-shot configurations and we test their robustness on different datasets. Our generated patterns achieve a significant performance increase on LOGIC, validating the effectiveness of this structural approach.

Paper Type: Long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: rhetoric and framing, argument schemes and reasoning

Contribution Types: Model analysis & interpretability

Languages Studied: English

Reassignment Request Area Chair: This is not a resubmission

Reassignment Request Reviewers: This is not a resubmission

A1 Limitations Section: This paper has a limitations section.

A2 Potential Risks: Yes

A2 Elaboration: 9

B Use Or Create Scientific Artifacts: Yes

B1 Cite Creators Of Artifacts: Yes

B1 Elaboration: 3, 5

B2 Discuss The License For Artifacts: Yes

B2 Elaboration: 9

B3 Artifact Use Consistent With Intended Use: Yes

B3 Elaboration: 9

B4 Data Contains Personally Identifying Info Or Offensive Content: Yes

B4 Elaboration: 9

B5 Documentation Of Artifacts: Yes

B5 Elaboration: 3

B6 Statistics For Data: Yes

B6 Elaboration: 3

C Computational Experiments: Yes

C1 Model Size And Budget: Yes

C1 Elaboration: 5

C2 Experimental Setup And Hyperparameters: Yes

C2 Elaboration: 5

C3 Descriptive Statistics: Yes

C3 Elaboration: 4,5,6

C4 Parameters For Packages: N/A

D Human Subjects Including Annotators: No

D1 Instructions Given To Participants: N/A

D2 Recruitment And Payment: N/A

D3 Data Consent: N/A

D4 Ethics Review Board Approval: N/A

D5 Characteristics Of Annotators: N/A

E Ai Assistants In Research Or Writing: No

E1 Information About Use Of Ai Assistants: N/A

Author Submission Checklist: yes

Submission Number: 1332