Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling (2024)

Abstract: Prior research in computational argumentation has mainly focused on scoring the quality of arguments, with less attention on explicating logical errors. In this work, we introduce four sets of explainable templates for common informal logical fallacies designed to explicate a fallacy’s implicit logic. Using our templates, we conduct an annotation study on top of 400 fallacious arguments taken from LOGIC dataset and achieve a high agreement score (Krippendorf’s \alpha of 0.54) and reasonable coverage 83%. Finally, we conduct an experiment for detecting the structure of fallacies and discover that state-of-the-art language models struggle with detecting fallacy templates (0.47 accuracy). To facilitate research on fallacies, we make our dataset and guidelines publicly available.

## introduction a fallacy is an invalid or weak argument supported by unsound reasoning @xcite . the automatic detection of fallacies has important applications, including providing constructive feedback to learners in writing. the assessment of argument quality, including fallacy detection, is considered an important topic in the fields of computational argumentation and argumentation mining @xcite . previous work on quality assessment has focused on numerical scoring @xcite and fallacy type-labeling tasks @xcite , without aiming to analyze fallacy logic structures, namely the representation of how given arguments are weak. in the field of argumentation theory, a typology of invalid arguments has been long studied and compiled into an inventory @xcite . the inventory typically includes semi-formal definitions and some examples for each type of fallacy. for example, faulty generalization is a widely recognized fallacy type, characterized by "drawing a (b) argumentation schemes @xcite claim: 𝑨 should not be brought about. template: argument from consequences-at-s3 template: faulty generalization #2 advanced courses argument: i took an nlp class, an advanced course in stanford. i suggest not taking further because they will hurt your gpa. conclusion based on a small sample size, rather than looking at statistics that are much more in line with the typical or average situation." @xcite . the semi-formal definition is as follows: "(i) sample s is taken from population p . (ii) sample s is a very small part of population p . (iii) conclusion c is drawn from sample s and applied to population p ". although such inventory provides insights into how the analysis of fallacy logic structure can be formulated as an nlp task, several important questions remain: (i) how should the annotation scheme for fallacy logic structure identification be designed? (ii) can humans consistently annotate fallacy logic structures? (iii) to what extent is the automatic identification of fallacy logic structure a challenging task for machines? to address this issue, we propose fallacy logic structure identification, a new task for identifying the underlying logical structure of fallacies. for this task, we design an annotation scheme and conduct an annotation study to examine its feasibility. the key idea behind our annotation scheme is to enrich previous work on the argumentative structure with a fallacy structure from an inventory of common fallacy types. consider the argument in fig. 1 , where the writer persuades people not to take advanced courses at stanford because they claim it will hurt their gpa. the claim is further supported by the writer's own, single experience based on their nlp class. this is a faulty generalization caused by the writer implicitly assuming that their single experience can be generalized to everyone. previous work in fallacy identification @xcite would identify this argument as faulty generalization (fig. 1 (a) ), but no additional information such as logical structure or fallacious reasoning is provided. argumentation schemes @xcite , a well-known typology for the representation of arguments, would categorize this argument as argument from consequences (fig. 1 (b) ), and @xcite 's argument templates, an operationalized version of argumentation schemes, represent this argument with a more fine-grained, logical representation by structured templates (fig. 1 (b' )). to represent the committed fallacy structure, our work further enriches this representation by adding an additional premise that indicates how the given argument is fallacious (fig. 1 (c )). our main contributions are as follows: ## related work fallacy annotation several studies address creating benchmarks for fallacy identification, including game facilitation @xcite and argumentation corpora validation @xcite . particularly, jin et al. (2022) focused on logical fallacies within climate change discourse, emphasizing the challenges posed by complex scientific data. they developed detailed annotation guidelines to aid in consistent identification of fallacies within climate arguments. similarly, goffredo et al. (2023) analyzed fallacious reasoning in u.s. presidential debates, highlighting common fallacies. they employed advanced computational techniques and the inception platform for structured annotation, ensuring reliability through cross-verification and krippendorff's α. in addition to the current benchmarks, our research proposes benchmark resources aimed at capturing fallacy structure rather than solely identifying fallacies. our research fills the gap, extending previous work by focusing on template annotation to capture the underlying structure of fallacious arguments. argumentation structure argumentation theory examines how arguments, including those about daily exercise, are constructed and evaluated. to begin with, @xcite established methods for parsing argumentation structure in persuasive essays by identifying and classifying argument components and their relationships. toulmin (2003) provided a framework for analyzing arguments by decomposing them into components like claim, grounds, warrant, and rebuttal. @xcite focused on specific argumentation schemes, such as argument from analogy, which compares similar situations to infer outcomes but risks failure with irrelevant similarities (false analogy). the argument from consequences @xcite scheme emphasizes potential outcomes of actions, often involving causality and appeals no template can be instantiated #1 a a should not be brought about good(c) x suppress support fallacy of credibility promote #2 #3 #4 #5 a should not be brought about a should be brought about a should be brought about support support support a bad(c) x suppress promote a good(c) x promote promote a bad(c) x promote promote no template can be instantiated #1 a a should not be brought about good(c) suppress support false causality support #2 #3 #4 #5 a should be brought about a should not be brought about a should be brought about support support support a bad(c) promote a good(c) promote a bad(c) suppress a good(c) related to support a bad(c) related to support a good(c) related to support a bad(c) related to no template can be instantiated a a should be brought about good(c) promote support false dilemma #5 a should be brought about a should be brought about a should be brought about support support a bad(c) suppress a bad(c) promote #1 #2 support a good(c) suppress #3 #4 ¬a good(c) suppress support ¬a bad(c) promote support ¬a good(c) promote support ¬a bad(c) suppress support no template can be instantiated #1 a a should not be brought about good(c) suppress support faulty generalization support #2 #3 #4 #5 a should be brought about a should not be brought about a should be brought about support support support a bad(c) promote a good(c) promote a bad(c) suppress a|a' good(c|c') suppress support a|a' bad(c|c') promote support a|a' good(c|c') promote support a|a' bad(c|c') suppress figure 2: our templates for annotating fallacious argument logical structure. we extend upon existing work @xcite , consisting of a conclusion (i.e., a should (not) be brought about) and supporting premise, by adding an additional supporting premise in bold which represents the committed fallacy logical structure. to consequences. evaluating it requires considering 1) the connection between action and consequence, 2) the quality of supporting evidence, and 3) whether opposing consequences have been addressed. building on prior work on argument structure, particularly the argument from consequences scheme (a frequently used scheme by @xcite , this research addresses a gap by using argument templates, inspired by @xcite to capture the structure of fallacies within this scheme. this choice is motivated by the scheme's frequent use and its potential for revealing fallacious arguments. building on this potential, and inspired by @xcite 's templates, we address a gap by using templates to capture the structure of fallacies within the argument from the consequence scheme. previous work on argument from consequences demonstrates high coverage in annotation efforts, further supporting this approach. ## fallacy logic structure 3.1 design principles to develop an annotation scheme for fallacy logic structure, we adhere to three key criteria. first, we require the annotation to be able to explain the underlying structure of fallacy. we extend the existing representation of arguments (fig. 1 (b' )) by an additional premise attached with an explanation as to why it fallaciously supports the original premise (fig. 1 (c) ). second, our annotation scheme must cover the majority of instances of common fallacy types. we focus on the fallacies most commonly studied in computational argumentation, such as those in @xcite and @xcite , whose statistics on fallacy types guide our template design to match the most frequent occurrences. we develop 20 new templates covering four defective induction fallacy types: fallacy of credibility, false causality, false dilemma, and faulty generalization. an example and more detailed explanation regarding the types can be found in section a.2. third, our annotation scheme must utilise reisert et al. (2018)'s template selection and slot-filling approach further simplifying annotation while remaining computationally friendly. as inspired by the argument from consequences and employing @xcite 's work as a base scheme, the template design captures both positive and negative consequences within the scheme. this results in two templates for each consequence type, along with a template addressing instances that cannot be directly covered. this approach aims to provide rich information about fallacy structures while simplifying the annotation process. ## flee the flaw (ftf) dataset we discuss the creation of our dataset flee the flaw (henceforth, ftf). first, we use an existing dataset of annotated fallacious arguments for creating our guidelines and building our inventory of fallacy templates. we then conduct a full-fledged annotation on top of 400 arguments. ## experiments to what extent is the automatic identification of fallacy logic structure challenging for machines? we evaluate current state-of-the-art llms for ftf. ## conclusion and future work in this work, we conduct the first study to address logical fallacy structure by creating an inventory of fallacy templates. in total, we created 20 novel templates spanned across 4 fallacy types (fallacy of credibility, false causality, false dilemma, and faulty generalization). we created flee the flaw, a new dataset consisting of 400 arguments from logic @xcite annotated with fallacy logic structure, and publicly released both the corpus and guidelines. our dataset achieved a high inter-annotator agreement (krippendorf's α of 0.54) and coverage (83%). we experiment on top of our new dataset by conducting in-context learning and fine-tuning for fallacy logic structure identification and discover that it is still a significant challenge for state-of-the-art language models. on top of logic, we also test our templates on 60 randomly sampled instances across four additional datasets which results in inter-annotator agreement (krippendorf's α of 0.45) and coverage (57%). our next step involves studying the underlying patterns and reasoning errors in arguments by analyzing the logical structure of fallacies. simultaneously, we plan to conduct large-scale annotation on top of lengthier, more natural arguments. finally, we plan to explore non-consequential topics and consider more argumentation schemes.