Fallacies are not only part and parcel of human communication, they are also important for generative models in that fallacies can be tailored to self-verify the output they generate. Previous work has shown that fallacy detection and classification is tricky, but the question that still remains is whether the inclusion of argumentation theory in prompting Large Language Models (LLMs) on the task enhances the performance of those models. In this paper we show that this is not the case: Using the pragmadialectics approach to fallacies (Van Eemeren and Grootendorst, 1987), we show that three GPT models struggle with the task. Based on our own PD-oriented dataset of fallacies and a carefully curated extension of an existing fallacy dataset from Jin et al. (2022a), we show that this is not only the case for fallacies "in the wild", but also for textbook examples of fallacious arguments. Our paper also supports the claim that LLMs generally lag behind in fallacy classification in comparison to smallerscale neural or even statistical models.
Being aware of frames, i. e., the aspect-based grouping of arguments, is crucial in applications that build upon a corpus of arguments, allowing, among others, biases and filter bubbles to be mitigated. However, manually identifying and naming these frames can be timeconsuming and therefore not feasible for larger datasets. Within this work, we present a sequential three-step pipeline for automating this task in a data-driven manner. After embedding the arguments, we apply clustering algorithms for identifying the frames and subsequently, utilize methods from the field of cluster labeling to name the frames. The proposed approach is tailored towards the requirements of practical applications where arguments may not be easily split into their argumentative units and hence can belong to more than one frame. Performing a component-wise evaluation, we determine the best-performing configuration of the pipeline. Our results indicate that frames should be identified by performing overlapping and not exclusive clustering and the naming of frames can be accomplished best by extracting aspect terms and weighting them with c-TF-IDF.
A crucial task when analyzing arguments is to determine their quality. Especially when you have to choose from a large number of suitable arguments, the determination of a reliable argument quality value is of great benefit. Probably the best-known model for determining such an argument quality value was developed in IBM's Project Debater and made available to the research community free of charge via an API. In fact, the model was never open and the API is no longer available. In this paper, IBM's model is reproduced using the freely available training data and the description in the corresponding publication. Our reproduction achieves similar results on the test data as described in the original publication. Further, the predicted quality scores of reproduction and original show a very high correlation (Pearson's r = 0.9) on external data.
To encourage computational argumentation through critical question generation (CQs-Gen), we propose an ACL 2025 CQs-Gen shared task system to generate critical questions (CQs) with the best effort to counter argumentative text by discovering logical fallacies, unjustified assertions, and implicit assumptions. Our system integrates a quantized language model, semantic similarity analysis, and a meta-evaluation feedback mechanism including the key stages such as data preprocessing, rationale-augmented prompting to induce specificity, diversity filtering for redundancy elimination, enriched meta-evaluation for relevance, and a feedback-reflect-refine loop for iterative refinement. Multi-metric scoring guarantees highquality CQs. With robust error handling, our pipeline ranked 7th among 15 teams, outperforming baseline fact-checking approaches by enabling critical engagement and successfully detecting argumentative fallacies. This study presents an adaptive, scalable method that advances argument mining and critical discourse analysis.
Television debates play a key role in shaping public opinion, however, the rapid exchange of viewpoints in these settings often makes it difficult to perceive the underlying nature of the discussion. While there exist several debate visualisation techniques, to the best of our knowledge, none of them emphasise the argumentative dynamics in particular. With DEBARGVIS, we present a new interactive debate visualisation tool that leverages data annotated with argumentation structures to demonstrate how speaker interactions unfold over time, enabling users to deepen their comprehension of the debate.
Definition generation models trained on dictionary data are generally expected to produce neutral and unbiased output while capturing the contextual nuances. However, previous studies have shown that generated definitions can inherit biases from both the underlying models and the input context. This paper examines the extent to which stance-related bias in argumentative data influences the generated definitions. In particular, we train a model on a slang-based dictionary to explore the feasibility of generating persuasive definitions that concisely reflect opposing parties' understandings of contested terms. Through this study, we provide new insights into bias propagation in definition generation and its implications for definition generation applications and argument mining.
This paper presents the CriticalBrew submission to the CQs-Gen 2025 shared task, which focuses on generating critical questions (CQs) for a given argument. Our approach employs a multi-agent framework containing two sequential components: 1) Generation: machine society simulation for generating CQs and 2) Evaluation: LLM-based evaluation for selecting the top three questions. The first models collaboration as a sequence of thinking patterns (e.g., debate -> reflect). The second assesses the generated questions using zero-shot prompting, evaluating them against several criteria (e.g., depth). Experiments with different open-weight LLMs (small vs. large) consistently outperformed the baseline, a single LLM with zero-shot prompting. Two configurations, agent count and thinking patterns, significantly impacted the performance in the shared task's CQ-usefulness evaluation, whereas different LLM-based evaluation strategies (e.g., scoring) had no impact. Our code is available on GitHub(1).
This paper addresses the problem of crossregister generalization in argument mining within political discourse. We examine whether models trained on adversarial, spontaneous U.S. presidential debates can generalize to the more diplomatic and prepared register of UN Security Council (UNSC) speeches. To this end, we conduct a comprehensive evaluation across four core argument mining tasks. Our experiments show that the tasks of detecting and classifying argumentative units transfer well across registers, while identifying and labeling argumentative relations remains notably challenging, likely due to register-specific differences in how argumentative relations are structured and expressed. As part of this work, we introduce ArgUNSC, a new corpus of 144 UNSC speeches manually annotated with claims, premises, and their argumentative links. It provides a resource for future in- and cross-domain studies and novel research directions at the intersection of argument mining and political science.
We present an overview of the MMArgFallacy2025 shared task on Multimodal Argumentative Fallacy Detection and Classification in Political Debates, co-located with the 12th Workshop on Argument Mining at ACL 2025. The task focuses on identifying and classifying argumentative fallacies across three input modes: text-only, audio-only, and multimodal (text+audio), offering both binary detection (AFD) and multi-class classification (AFC) subtasks. The dataset comprises 18,925 instances for AFD and 3,388 instances for AFC, from the MM-USED-Fallacy corpus on U.S. presidential debates, annotated for six fallacy types: Ad Hominem, Appeal to Authority, Appeal to Emotion, False Cause, Slippery Slope, and Slogan. A total of 5 teams participated: 3 on classification and 2 on detection. Participants employed transformer-based models, particularly RoBERTa variants, with strategies including prompt-guided data augmentation, context integration, specialised loss functions, and various fusion techniques. Audio processing ranged from MFCC features to state-of-the-art speech models. Results demonstrated textual modality dominance, with best text-only performance reaching 0.4856 F1-score for classification and 0.34 for detection. Audio-only approaches underperformed relative to text but showed improvements over previous work, while multimodal fusion showed limited improvements. This task establishes important baselines for multimodal fallacy analysis in political discourse, contributing to computational argumentation and misinformation detection capabilities.
This paper presents a new system for generating critical questions in debates, developed for the Critical Questions Generation shared task. Our two-stage approach, combining generation and classification, utilizes LLaMA 3.1 Instruct models (8B, 70B, 405B) with zero/few-shot prompting. Evaluations on annotated debate data reveal several key insights: few-shot generation with 405B yielded relatively high-quality questions, achieving a maximum possible punctuation score of 73.5. The 70B model outperformed both smaller and larger variants on the classification part. The classifiers showed a strong bias toward labeling generated questions as Useful, despite limited validation. Further, our system, ranked 6th, outperformed baselines by 3%. These findings stress the effectiveness of large-sized models for question generation and medium-sized models for classification, and suggest the need for clearer task definitions within prompts to improve classification accuracy.
Critical Question Generation (CQs-Gen) improves reasoning and critical thinking skills through Critical Questions (CQs), which identify reasoning gaps and address misinformation in NLP, especially as LLM-based chat systems are widely used for learning and may encourage superficial learning habits. The Shared Task on Critical Question Generation, hosted at the 12th Workshop on Argument Mining and co-located in ACL 2025, has aimed to address these challenges. This study proposes a CQs-Gen pipeline using Llama-3-8B-Instruct-GGUF-Q8_0 with few-shot learning, integrating text simplification, NER, and argument schemes to enhance question quality. Through an extensive experiment testing without training, fine-tuning with PEFT using LoRA on 10% of the dataset, and few-shot fine-tuning (using five examples) with an 8-bit quantized model, we demonstrate that the fewshot approach outperforms others. On the validation set, 397 out of 558 generated CQs were classified as Useful, representing 71.1% of the total. In contrast, on the test set, 49 out of 102 generated CQs, accounting for 48% of the total, were classified as Useful following evaluation through semantic similarity and manual assessments.
Classifying argumentative fallacies in political discourse is challenging due to their subtle, persuasive nature across text and speech. In our MM-ArgFallacy Shared Task submission, Team NUST investigates uni-modal (text/audio) and multi-modal (text+audio) setups using pretrained models-RoBERTa for text and Whisper for audio. To tackle severe class imbalance, we introduce Prompt-Guided Few-Shot Augmentation (PG-FSA) to generate synthetic samples for underrepresented fallacies. We further propose a late fusion architecture combining linguistic and paralinguistic cues, enhanced with balancing techniques like SMOTE and Focal Loss. Our approach achieves top performance across modalities, ranking 1st in textonly and multi-modal tracks, and 3rd in audioonly, on the official leaderboard. These results underscore the effectiveness of targeted augmentation and modular fusion in multi-modal fallacy classification.
This paper explores the application of argument mining to mental health narratives using zero-shot transfer learning. We fine-tune a BERT-based sentence classifier on 15k essays from the Persuade dataset-achieving 69.1% macro-F1 on its test set-and apply it without domain adaptation to the CAMS dataset, which consists of anonymized mental health-related Reddit posts. On a manually annotated gold-standard set of 150 CAMS sentences, our model attains 54.7% accuracy and 48.9% macro-F1, with evidence detection (F1 = 63.4%) transferring more effectively than claim identification (F1 = 32.0%). Analysis across expert-annotated causal factors of distress shows that personal narratives heavily favor experiential evidence (65-77% of sentences) compared to academic writing. The prevalence of evidence sentences, many of which appear to be grounded in lived experiences, such as descriptions of emotional states or personal events, suggests that personal narratives favor descriptive recollection over formal, argumentative reasoning. These findings underscore the unique challenges of argument mining in affective contexts and offer recommendations for enhancing argument mining tools within clinical and digital mental health support systems.
Argumentative fallacy classification plays a crucial role in improving discourse quality by identifying flawed reasoning that may mislead or manipulate audiences. While traditional approaches have primarily relied on textual analysis, they often overlook paralinguistic cues such as intonation and prosody that are present in speech. In this study, we explore how multimodal analysis, in which we combine textual and audio features, can enhance fallacy classification in political debates. We develop and evaluate text-only, audio-only, and multimodal models using the MM-USED-fallacy dataset to assess the contribution of each modality. Our findings indicate that the multimodal model, which integrates linguistic and acoustic signals, outperforms unimodal systems, underscoring the potential of multimodal approaches in capturing complex argumentative structures.
Online discussions can either bridge differences through constructive dialogue or amplify divisions through destructive interactions. This paper proposes a computational approach to analyze dialogical relation patterns in YouTube comments, offering a fine-grained framework for controversy detection, enabling also analysis of individual contributions. Our experiments demonstrate that shallow learning methods, when equipped with theoreticallygrounded features, consistently outperform more complex language models in characterizing discourse quality at both comment-pair and conversation-chain levels. Ablation studies confirm that divisive rhetorical techniques serve as strong predictors of destructive communication patterns. This work advances understanding of how communicative choices shape online discourse, moving beyond engagement metrics toward nuanced examination of constructive versus destructive dialogue patterns.
Argument mining is the task of identifying the argument structure of a text: claims, premises, support/attack relations, etc. However, determining the complete argument structure can be quite involved, especially for unpolished texts from online forums, while for many applications the identification of argumentative key statements would suffice (e.g., for argument search). To this end, we introduce and investigate the new task of segmenting an argumentative text by its key statements. We formalize the task, create a first dataset from online communities, propose an evaluation scheme, and conduct a pilot study with several approaches. Interestingly, our experimental results indicate that none of the tested approaches (even LLM-based ones) can actually satisfactorily solve key statement segmentation yet.
Argument Mining (AM) aims at detecting argumentation structures (i.e., premises and claims linked by attack and support relations) in text. A natural application domain is political debates, where uncovering the hidden dynamics of a politician's argumentation strategies can help the public to identify fallacious and propagandist arguments. Despite the few approaches proposed in the literature to apply AM to political debates, this application scenario is still challenging, and, more precisely, concerning the task of predicting the relation holding between two argument components. Most of AM relation prediction approaches only consider the textual content of the argument component to identify and classify the argumentative relation holding among them (i.e., support, attack), and they mostly ignore the structural knowledge that arises from the overall argumentation graph. In this paper, we propose to address the relation prediction task in AM by combining the structural knowledge provided by a Knowledge Graph Embedding Model with the contextual knowledge provided by a fine-tuned Language Model. Our experimental setting is grounded on a standard AM benchmark of televised political debates of the US presidential campaigns from 1960 to 2020. Our extensive experimental setting demonstrates that integrating these two distinct forms of knowledge (i.e., the textual content of the argument component and the structural knowledge of the argumentation graph) leads to novel pathways that outperform existing approaches in the literature on this benchmark and enhance the accuracy of the predictions.
Psychological research has long suggested that storytelling can shape beliefs and behaviors by fostering emotional engagement and narrative transportation. However, it remains unclear whether these effects extend to online argumentative discourse. In this paper, we examine the role of narrative in real-world argumentation using discussions from the ChangeMyView subreddit. Leveraging an automatic story detection model, we analyze how narrative use varies across persuasive comments, user types, discussion outcomes, and the kinds of change being sought. While narrative appears more frequently in some contexts, it is not consistently linked to successful persuasion. Notably, highly persuasive users tend to use narrative less, and storytelling does not demonstrate increased effectiveness for any specific type of persuasive goals. These findings suggest that narrative may play a limited and context-dependent role in online discussions, highlighting the need for computational models of argumentation to account for rhetorical diversity.
The proliferation of AI technologies has reinforced the importance of developing critical thinking skills. We propose leveraging Large Language Models (LLMs) to facilitate the generation of critical questions: inquiries designed to identify fallacious or inadequately constructed arguments. This paper presents an overview of the first shared task on Critical Questions Generation (CQs-Gen). Thirteen teams investigated various methodologies for generating questions that critically assess arguments within the provided texts. The highest accuracy achieved was 67.6, indicating substantial room for improvement in this task. Moreover, three of the four top-performing teams incorporated argumentation scheme annotations to enhance their systems. Finally, while most participants employed open-weight models, the two highest-ranking teams relied on proprietary LLMs.(1)
