Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition (2024)

Abstract: Recognizing fallacies is crucial for ensuring the quality and validity of arguments across various domains. However, computational fallacy recognition faces challenges due to the diverse genres, domains, and types of fallacies found in datasets. This leads to a highly multi-class, and even multi-label, setup with substantial class imbalance. In this study, we aim to enhance existing models for fallacy recognition by incorporating additional context and by leveraging large language models to generate synthetic data, thus increasing the representation of the infrequent classes. We experiment with GPT3.5 to generate synthetic examples and we examine the impact of prompt settings for this. Moreover, we explore zero-shot and few-shot scenarios to evaluate the effectiveness of using the generated examples for training smaller models within a unified fallacy recognition framework. Furthermore, we analyze the overlap between the synthetic data and existing fallacy datasets. Finally, we investigate the usefulness of providing supplementary context for detecting fallacy types that need such context, e.g., diversion fallacies. Our evaluation results demonstrate consistent improvements across fallacy types, datasets, and generators. The code and the synthetic datasets are all publicly available.

## introduction fallacies are common errors in reasoning that can mislead and invalidate arguments. the capacity to discern fallacies is fundamental to sustaining the robustness and authenticity of arguments across various domains, such as public policy, legal reasoning, and scientific discourse @xcite . in recent years, the task of automated fallacy recognition has attracted significant interest from researchers in the fields of natural language processing (nlp) and artificial intelligence (ai) 1 https://github.com/tariq60/fallacy-detection @xcite @xcite @xcite . however, numerous challenges persist, including the multiplicity of genres, domains, and fallacy types, which contribute to a complex multiclass and multi-label task structure compounded by class imbalances in datasets. existing work on fallacy recognition is still in its early stages, with limited datasets available. these datasets cover different types of fallacies in various contexts, such as question and answer dialog moves @xcite , name-calling in social media debates @xcite , logical fallacies from educational websites @xcite , and fallacies related to covid-19 misinformation in social media and news @xcite . previous work has focused on detecting fallacies in individual datasets, using techniques like fine-tuning transformers for sequence tagging @xcite , and training structure-aware classifiers @xcite . however, fallacy recognition is challenging due to the high number of classification labels, class imbalance in datasets, limited dataset sizes, and poor out-of-distribution generalization. @xcite proposes a multitask framework using t5, which converts fallacy types into natural language instructions, and thus approaches the differences between fallacy datasets as different tasks, but their approach does not detect infrequent classes effectively. @xcite incorporate argumentation features to detect fallacies in political debates, while @xcite trains a structure-aware classifier on fallacies from educational websites; however, they both focus on a single fallacy scheme from one dataset while we include multiple fallacy schemes. current state-of-the-art models struggle with the recognition of underrepresented fallacies, which may often require additional context for accurate identification, such as diversion fallacies @xcite . this necessitates a comprehensive and diverse dataset for training these models. one strategy to combat the challenge of sparse and imbalanced data in machine learning is data augmentation @xcite by creating synthetic examples, thereby enhancing the dataset size and diversity and improving the performance of the machine learning model. large language models (llms) such as gpt-3, 3.5, 4 @xcite , llama @xcite and @xcite have shown promising zero-shot performance on various text classification tasks @xcite . however, they still do not perform as good as smaller models that are fine-tuned for specific tasks, especially ones that require deeper understanding and reasoning @xcite . for complex tasks, such as fallacy recognition, they can be used for data augmentation by bootstrapping existing human-annotated datasets by annotating or generating additional examples @xcite @xcite . we extend previous work on fallacy recognition by exploring the capabilities of large language models to generate synthetic data that augments manually labeled datasets (section 3). we study the effect of the data generated under zero-shot and few-shot conditions on the downstream task of fallacy recognition and show our experimental setup (section 4) and results (section 5). we also analyze the quality of the synthetic data and its similarity to the fallacy datasets (section 6). figure 1 shows an overview of our approach of using gpt-3.5 to generate additional training examples in zero/fewshot settings, then training a t5 model @xcite for fallacy recognition on a combination of the original and the synthetic data. ## fallacy datasets we experiment with the five fallacy datasets covered by @xcite . they include fallacies in question-answer pairs in game settings (argotario) @xcite , 18 propaganda techniques in news articles (propaganda) @xcite , logical fallacies from educational websites (logic) @xcite , and fallacies in misinformation around covid in social media (covid) @xcite and climate change news articles (climate) @xcite . these datasets identify different fallacy types and range from 5 to 18 fallacies. @xcite unified the fallacy types from the four schemes and introduced 28 fallacy types in one unified scheme. these dataset are different in size as they go from a few hundred examples (450-880) for climate, covid and argotario, to a few thousands (4,500 to 5,100) for logic and pro-paganda. the total number of examples per fallacy type varies significantly as it ranges from less than 100 examples for some fallacies (e.g., false analogy, strawman, whataboutism) to more than 1,000 examples (e.g., hasty generalization, name calling or labeling, loaded language). detailed numbers for each fallacy per dataset and split can be found in @xcite . one main challenge in these datasets is the high imbalance frequency of classes in a high multiclass, and even multi-label task. the unified model presented by @xcite improves the overall results but still performs much better on more frequent classes, thus we utilize data augmentation to address this challenge. in addition, two of the five fallacy datasets: propaganda and cli-mate are from news articles where the fallacy is annotated at the sentence or fragment level. therefore, we assess the benefit of providing additional context to the fallacious segment (sentence or fragment) by including the preceding or succeeding sentence when available. ## synthetic data generation to generate additional examples for infrequent fallacy classes, we leverage gpt-3.5-turbo (henceforth called gpt-3.5), a conversational language model, as a data augmentation tool. we explore zero-shot, 1-shot, 2-shot, and 5-shot settings to generate examples that have not been seen in the original training data. these generated examples provide diversity and help address the data scarcity issue for less frequent fallacy types. in order to understand the capabilities of pretrained large language models such as gpt-3.5 in producing synthetic data, we control the information provided in the prompts as follows: i) zeroshot prompts that have no fallacy example and ask the model to generate an example in one form (e.g., sentence, tweet, question-answer pair) for a certain fallacy provided in the prompt; ii) few-shot prompts that list the fallacy type and output form in addition to providing 1 to 5 examples for the given fallacy type in the prompt. the model is asked to generate the same number of examples given in the prompt (i.e. 1-shot prompt asks the model to generate 1 example, 5-shot ask for 5 new examples and so on); iii) few-shot-context prompts that provide the examples of fallacy and their wider context if available (previous and next sentence) and asks the language model to do the same by generating both examples for a certain fallacy and their contexts. figure 2 shows an example of the 1-shot-context prompt of the irrelevant authority fallacy from the propaganda dataset. for all data augmentation settings, we generate the same number of examples per fallacy and thus study the quality of the synthetic data using different prompts for generation. the aim is to address data imbalance in these datasets so we exclude fallacies that are very frequent, especially if they only exist in one fallacy scheme (i.e. less diverse). following this criteria, we exclude loaded language and name calling or labeling that only appear in propaganda. we also do not generate examples for hasty generalization in a form similar to the logic dataset, but we generate ones in covid-19 and climate change domains since their respective datasets have this fallacy in very low counts. ## experimental setup similar to @xcite , we use the t5 model @xcite , a versatile text-to-text transformer, as the backbone for fallacy recognition by fine-tuning instruction-based prompts on all fallacy datasets. the prompts are designed to provide explicit instructions on identifying specific fallacies, enabling targeted learning within the model. this approach is inline with a large body of research that utilizes instruction-tuning of large language models on many tasks @xcite . we evaluate the proposed approach on the five fallacy datasets. we train the t5 model using a combination of the original labeled data and the generated examples from gpt-3.5. we compare the performance of the model under different settings, including zero-shot, 1-shot, 2-shot, and 5-shot scenarios, with and without additional context to understand the impact of prompt and data availability on fallacy recognition. all fallacy examples, original and synthetic, are transformed into instruction-based prompts that are used to fine-tune the t5-3 billion model (henceforth t53b) in a multitask fashion. the model and hyperparameters are fixed and we only change the training data that is fed into the model with the aim to study the ability of a smaller size model such as t53b to learn from manually annotated or crowdsourced data as well as synthetically generated data by a larger size model such as gpt-3.5. we show the results of all training conditions for the propaganda and climate datasets in it was challenging for gpt-3.5 to provide usable examples with contexts in most cases without providing at least one example in the prompt for gpt-3.5 to follow. therefore zero-shot prompts are only reported in no-context settings. we discuss in the next section the effect of both data augmentation and the addition of context in more details. ## original and synthetic data similarity in order to understand the reason for 1-shot prompts to generate synthetic data that is more beneficial to the task, we analyze the similarity between the generated data and the original training examples shown at the prompts. for similarity, we use bleurt score as it has the most consistent results with human evaluation @xcite . we calculate bleurt score for each original-synthetic example pair where the original example is the one used in the prompt to generate the synthetic example. thus, we only report similarity in the 1-shot, 2-shot, and 5-shot prompts. for the 2-shot and 5-shot, we report the maximum score for a generated example with respect to all original examples included in the prompt. figure 3 shows average bleurt scores for each fallacy in all five datasets. we notice high similarity scores in argotartio and logic that range between 0.50 and 0.30 and much lower scores for climate and covid that range between 0.18 and 0.30. this shows that it is harder to produce synthetic examples that are similar to naturallyoccurring examples in news and social media. common across all datasets is that on average 1-shot prompts for most fallacies tend to produce less similar examples to the ones included in the prompts when compared with 2-shot and 5-shot prompts. however, the synthetic data from the 1shot prompts is more useful to training a model for fallacy recognition that is tested on humanannotated data. after closer examination of synthetic data from 2-shot and 5-shot, while they are more similar to the original data on average, they tend be more similar to one of the 2 example (or 5 examples) provided in the prompt and thus suffer from repetition of one form, which makes the overall synthetic data less diverse. this shows the sensitivity of the llm to the information in the prompt and possibly to the order of the provided few-shot examples. therefore, asking an llm to generate multiple synthetic examples in one instruction could lead it to generate ones that are more similar to each other even if the original data in the prompt include multiple human-annotated examples. ## related work we provide an overview of the literature that discusses the use of llms for data augmentation, and the literature on the development of models and resources for fallacy recognition. ## conclusion and future work fallacy recognition remains a challenging problem due to the high number of classes, severe data imbalance and the need in some cases for external information to the fallacious segment. to mitigate the effect of data imbalance, we studied the capabilities of large language models to generate synthetic data that can be used to train smaller models on a combination of original and synthetic data for fallacy recognition across multiple tasks. the main observation is that data augmentation through large language models is beneficial for this task. however, the conditions under which the data is generated impacts the quality of the synthetic data significantly. providing one example in the prompt (1-shot) for a certain fallacy from the original data and asking gpt-3.5 to generate a similar example results in synthetic data that is more beneficial to downstream models for fallacy recognition tested on data from the same distribution. the value in having synthetic data that is less similar to the original training data and possibly more generic to the task needs to be tested on data from unseen fallacy schemes or domains, which presents a potential avenue for future work. overall, large language models show great potential to generate additional training data for the task of fallacy recognition, which can be used to train smaller size open-source models for this task. in future work, we want to test the resilience of data augmentation on out-of-domain test sets such as fallacy in political debates. also, we want to study the ability of llms to generate examples that could be labeled by multiple fallacies and train machine learning for this tasks with a multi-labeling objective. finally, we want to experiment with the ability of llms to provide more useful context for fallacy recognition.