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.
