PREDICT : Multi-Agent-based Debate Simulation for Generalized Hate Speech Detection (2024)

Abstract: While a few public benchmarks have been proposed for training hate speech detection models, the differences in labeling criteria between these benchmarks pose challenges for generalized learning, limiting the applicability of the models. Previous research has presented methods to generalize models through data integration or augmentation, but overcoming the differences in labeling criteria between datasets remains a limitation. To address these challenges, we propose PREDICT, a novel framework that uses the notion of multi-agent for hate speech detection. PREDICT consists of two phases: (1) PRE (Perspective-based REasoning): Multiple agents are created based on the induced labeling criteria of given datasets, and each agent generates stances and reasons; (2) DICT (Debate using InCongruenT references): Agents representing hate and non-hate stances conduct the debate, and a judge agent classifies hate or non-hate and provides a balanced reason. Experiments on five representative public benchmarks show that PREDICT achieves superior cross-evaluation performance compared to methods that focus on specific labeling criteria or majority voting methods. Furthermore, we validate that PREDICT effectively mediates differences between agents’ opinions and appropriately incorporates minority opinions to reach a consensus. Our code is available at https://github.com/Hanyang-HCC-Lab/PREDICT

## introduction the rise of hate speech on the internet has become a significant social issue, prompting extensive research on hate speech detection @xcite @xcite . one of the main difficulties encountered in hate speech detection is generalization @xcite . this refers to the situation where an effective model *corresponding author trained on a particular dataset may perform poorly when the model is applied to a different dataset @xcite . this is mainly due to differences in various labeling criteria @xcite , including the purpose (identifying hate speech in social and historical contexts, classifying sentiments to classify hate speech, or focusing on the targets of hate speech), the labeling method (the number of annotators, the labeling process, and guidelines), the granularity of the labels (multiclass or binary), the size of the dataset, and the time and method of data collection @xcite . thus, an approach that does not overly rely on specific labeling criteria is needed to improve the generalization of hate speech detection. previous studies have attempted to address the issue of generalization in hate speech detection through various approaches, including data integration, augmentation, and explanation generation. the integration of datasets covering diverse topics, such as gender and race @xcite , allows the model to learn more extensive hate speech patterns. however, differences in the labeling of the datasets can cause sentences with similar words or expressions to be labeled differently, which can confuse the model regarding consistency. the recent data augmentation through gpt-2 @xcite allows the model to learn various forms of hate speech. however, this approach has a limitation in that it may result in the generation of repetitive patterns that exist in the original datasets. the use of gpt-generated explanations in training @xcite also depends on specific criteria for labeling data, resulting in underperformance on datasets based on different labeling criteria. while these approaches of previous research have partially improved the performance of hate speech detection, there remain limitations in effectively incorporating differences in labeling criteria into the model's training or inference process. our research employs a pluralistic approach @xcite to build consensus based on respect and inclusion of diverse perspectives and to address the overfitting caused by different labeling criteria across datasets. recent social science research emphasizes the value of pluralism in addressing hate speech debates @xcite , advocating the inclusion of diverse values and perspectives and highlighting the need for rational integration of these views to achieve social consensus @xcite . in this paper, we present a predict framework that uses a large language model (llm) to construct a debate environment among different agents, where each agent has its independent perspectives based on the labeling criteria of the hate speech dataset, and simulates pluralistic decision-making in hate speech detection. predict consists of two phases: the "perspective-based reasoning (pre)" phase and the "debate using incongruent references (dict)" phase. the former is designed to form a perspective based on the assigned labeling criteria and similar contexts. the latter, in turn, simulates a debate between two debaters, with the judge providing the final label and justification. the dict phase is structured to reach a final decision through two rounds of debate motivated by @xcite and @xcite . we conducted experiments on five public benchmarks (five different agents) in the hate speech domain to quantitatively evaluate the performance of predict in the context of generalized hate speech detection (section 4). our results show the significant effectiveness of predict in accurately detecting hate speech in all five datasets and in deriving a strong justification for the decision. in summary, our study highlights the importance of consensus in hate speech research and demonstrates the value of multiple perspectives as a way to improve the accuracy of hate speech detection. our contributions are as follows: ## method figure 2 provides an overview of the predict framework, which consists of two main phases: (a) pre: a phase that generates agents' stances and reasons based on each refined independent perspective for a given text, and (b) dict: a phase in which debaters simulate a debate based on their stances (i.e., hate, non-hate) and reach a consensus for hate speech detection. in the dict phase, the agents are divided into two camps based on their stances. the debaters then combine the reasons from each camp to simulate the debate. the debate is conducted in two rounds, and finally, a judge makes the decision about hate or non-hate and generates a balanced reason that respects the arguments of each stance. ## conclusion this paper proposes the predict framework, a novel multi-agent-based debate simulation that employs a pluralistic approach to overcome differences in labeling criteria across datasets in the domain of hate speech detection. experimental results on five benchmark datasets show that predict performed best in cross-evaluations, demonstrating improved generalization through the integration of diverse perspectives. the results of this study indicate that the predict has the potential to be applied beyond hate speech detection, providing a new direction for llm-integrated research in the field of social sciences.