LLaMAntino against Cyber Intimate Partner Violence
Authors: Pierpaolo Basile, Marco de Gemmis, Marco Polignano, Giovanni Semeraro, Lucia Siciliani, Vincenzo Tamburrano, Fabiana Battista, Rosa Scardigno (Università di Bari Aldo Moro, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
Intimate Partner Violence refers to the abusive behaviours perpetrated on their own partner. This social issue has witnessed an increase over time, particularly after Covid-19. IPV can be circumscribed into two broad categories known as Intimate Partner Violence (IPV) and Cyber Intimate Partner Violence (C-IPV). Social Media and technologies can exacerbate these types of behaviours, but some ``digital footprints'', such as textual conversations, can be exploited by Artificial Intelligence models to detect and, in turn, prevent them. With this aim in mind, this paper describes a scenario in which the Italian Language Model family LLAmAntino can be exploited to explain the presence of toxicity elements in conversations related to teenage relationships and then educate the interlocutor to recognize these elements in the messages received.

Introduction
Research indicates that the most prevalent form of violence is that directed toward one's partner, known as Intimate Partner Violence (IPV). Early detection of these behaviours can be instrumental in mitigating their occurrence. One of the most critical aspects of this kind of behaviour is that victims often face challenges in identifying harmful behaviours due to their close relationship with the perpetrator. Misconceptions about romantic relationships, often due to old cultural stereotypes, such as the belief that certain behaviours are normal or acceptable, can further complicate the recognition of harmful actions. In today's society, the widespread use of social media and digital platforms has evolved this issue into Cyber Intimate Partner Violence (C-IPV) and often allows the perpetrators to gain greater control over their victims by constantly monitoring their locations or interactions with other people. Contrary to common belief, these technologies can be used to address the issue of violence. In fact, building AI models to identify potential violence-related behaviours is essential, and often, it provides the only means to act promptly and in real-time. Having such a tool can serve as a preventive measure against the escalation of harmful situations, for example, by integrating it into instant messaging apps and raising alerts where harmful content is detected. In this paper, we aim to utilize Large Language Models (LLMs) as tools that can not only identify but also explain toxic elements in intimate conversations. More specifically, we use a dataset of conversations about teenage relationships written in Italian that has been accurately annotated by human experts. Given LLMs' capability to tackle several downstream tasks, our goal is to explore the impact of different kinds of prompts on the generation of precise explanations. The paper is structured as follows: in Section sec:relatedwork, we provide a frame of what is intimate partner violence, the different forms, and the deleterious intra and interpersonal consequences. Moreover we also provide an overview of the methods proposed in the literature. Section sec:explanations focuses on the task of explaining toxic language in the context of IPV. We describe the dataset and the different types of annotations provided by researchers in General Psychology, as well as the prompting strategy adopted to instruct the language model. Finally, in Section sec:conclusions, we draw some conclusions and discuss directions for the continuation of the work.

Conclusion
In this paper, we presented our proposal to adopt our LLM to identify and describe toxic elements in discussions concerning teenage relationships. In particular, the LLM was used to generate explanations that describe why a sentence, in the context of an intimate relationship, can be toxic and constitute abuse. The main outcome of our preliminary investigation is that, even with few-shot prompting, the LLM learns to provide good explanations that adhere to a standard provided by expert psychologists. By exploiting LLMs' proficiency in processing and understanding human language, our approach seeks to go beyond just detection, aiming to grasp underlying motivations and factors contributing to the emergence of harmful behaviours. In future works, we intend to perform fine-tuning steps to better adapt LLMs to the specific task at hand. We also plan to investigate how different pre-training techniques and architectures can be leveraged to enhance model performance. Supervised fine-tuning AUTHOR, for instance, is a technique that can be employed to adapt the LLM to a specific task, such as generating explanations for abusive language, by using a labelled dataset. This approach can help the model to learn from its mistakes and to correct its biases, ultimately leading to improved performance. In the context of our study, supervised fine-tuning could be used to train the LLM on a dataset of abusive language explanations, to reduce the model's error rate and increase the quality of its responses. Direct Preferences Optimization (DPO) AUTHOR is another strategy that can be used to improve the performance of the LLM. DPO is a technique that allows the model to be trained directly on a set of user-provided preferences, such as the quality of the explanations it generates. This approach can be particularly effective in domains like abusive language, where the quality of the explanations is critical to ensure that the model does not perpetuate harmful biases. To ensure the effectiveness of our approach, we intend to confront our methodology with other models and incorporate further annotations to enhance the robustness and effectiveness of our methodology. This involves comparing the performance of our LLMs with other state-of-the-art models. Moreover, thanks to the collaboration with expert psychologists who are experts in the field to explore the application of Chain-of-Thought prompting techniques. \noindent We acknowledge the support of the PNRR project FAIR - Future AI Research (PE00000013), Spoke 6 - Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU. \noindent This Publication was produced with the co-funding of the European Union - Next Generation EU: NRRP Initiative, Mission 4, Component 2, Investment 1.3 - Partnerships extended to universities, research centres, companies and research D.D. MUR n. 341 del 15.03.2022 – Next Generation EU (PE0000014 - ''SEcurity and Rights In the CyberSpace - SERICS'' - CUP: H93C22000620001). \appendix