Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLM s in Hate Speech Countering (2024)

Abstract: The potential effectiveness of counterspeech as a hate speech mitigation strategy is attracting increasing interest in the NLG research community, particularly towards the task of automatically producing it. However, automatically generated responses often lack the argumentative richness which characterises expert-produced counterspeech. In this work, we focus on two aspects of counterspeech generation to produce more cogent responses. First, by investigating the tension between helpfulness and harmlessness of LLMs, we test whether the presence of safety guardrails hinders the quality of the generations. Secondly, we assess whether attacking a specific component of the hate speech results in a more effective argumentative strategy to fight online hate. By conducting an extensive human and automatic evaluation, we show how the presence of safety guardrails can be detrimental also to a task that inherently aims at fostering positive social interactions. Moreover, our results show that attacking a specific component of the hate speech, and in particular its implicit negative stereotype and its hateful parts, leads to higher-quality generations.

## introduction with the ever-increasing spread of social media platforms, online hate speech (hs) has become a crucial concern. a promising strategy to fight online hate is counterspeech (cs), which is defined as non-aggressive textual feedback that uses credible evidence, factual arguments, and alternative viewpoints @xcite . the potential effectiveness of counterspeech as a hate speech mitigation strategy has motivated an increasing interest in natural language generation (nlg) research towards the automation of its production @xcite . ## related work we consider three main relevant research areas: (i) studies on llms safety and performance, (ii) counterargument, and (iii) counterspeech generation. ## hate speech annotation in this section, we will first describe the hate speech data that we employed. then, we will describe the process to extract and annotate the premises, conclusions, and implied statement. finally, some statistics are provided on the obtained labels. ## counterspeech generation our goal is two-folded. firstly, we aim to compare the counterspeech generation of a model where safety guardrails are present (cs w/ ) to a model without guardrails (cs w/o ). secondly, we want to compare the effect on cs generation of four different rhetorical strategies, attacking different parts of the hate speech: the hateful premises/conclusion (cs hate ), the weakest point of its argumentation (cs weak ), its implied statement (cs is ), or the entire message as a baseline (cs base ). ## evaluation following, we describe the human evaluation setup and the automatic metrics that we employed. ## results and discussion in this section, we show the results foot_8 , grouped by safety configuration (cs w/ and cs w/o ), and attacking strategy (cs hate , cs weak , cs is , cs base ). in the following tables, the * symbol represents a statistically significant difference foot_9 , the best scores are in bold and the second best are underlined. ## conclusion in this work, we investigated various strategies to obtain cogent counterspeech. firstly, we tested whether the absence of safety guardrails has an impact on the perceived quality of the generated counterspeech. then, we used various attacking strategies, focusing on different aspects of the hate speech argument: its hateful premises/conclusion, its weakest argumentative component, its implied statement, and no component in particular. to do so, we used mistral, a model for which the presence of safety guardrails is most controllable. by conducting an extensive human and automatic evaluation, we show that the absence of guardrails has a positive effect on the perceived cogency of the is always one only element. to conclude, our work shows how the current implementation of safety guardrails might be suboptimal also for tasks that require high safety standards, such as counterspeech generation. in this perspective, by uncovering unintended pitfalls of safety guardrails, we highlight the necessity of better calibrating the helpfulness-harmlessness tradeoff, in order to further improve safety tuning in llms.