Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLMs in Hate Speech Countering (2410.03466v1)

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.

• The paper finds that removing safety guardrails improves the cogency of LLM-generated counterspeech without sacrificing perceived safety.
• The paper demonstrates that targeting specific hateful content, such as implied stereotypes, results in more effective counterspeech.
• The paper highlights that guardrail configurations are more decisive than rhetorical strategy, urging a reevaluation of AI safety trade-offs.

Analyzing the Impact of Guardrails on LLM Argumentative Strength in Counterspeech Generation

This paper explores the influence of safety guardrails within LLMs on their ability to generate effective counterspeech aimed at mitigating hate speech. The research tackles two primary questions: (1) Do safety guardrails compromise the cogency of generated counterspeech? (2) Does focusing on specific components of hate speech enhance the argumentative quality of these counterspeech responses?

The researchers employed Mistral Instruct, a model with manageable safety configurations, to explore these questions. They leveraged the White Supremacy Forum dataset for generating and annotating hate speech examples, focusing on their argumentative structures. The paper distinguished between various counterspeech strategies, including attacking the entire message versus targeting specific parts: the hateful text, the weakest premise or conclusion, or the implied negative stereotypes.

Upon analyzing both human and automatic evaluations, significant findings emerged:

1. Safety Guardrails and Cogency: It was observed that the absence of safety guardrails improved the cogency of counterspeech outputs without compromising perceived safety. This suggests that enhanced safety measures might inadvertently diminish the argumentative richness required for nuanced tasks like counterspeech.
2. Attacking Strategy: Targeting specific components of hate speech, particularly implied stereotypes and hateful statements, produced more effective counterspeech than non-specific, general attacks. The research indicates that an informed focus leads to higher-quality interactions, aligning more closely with expert human responses.
3. Overall Impact: Despite the variety of attacking strategies, the research shows that the presence of guardrails plays a more decisive role than the specific rhetorical strategy chosen regarding the perceived cogency of the counterspeech. This emphasizes the necessity for nuanced calibration of LLMs’ safety to sustain both their usefulness and safety.

The implications of these findings are substantial for the future design and alignment of LLMs. The research suggests a reevaluation of the helpfulness-harmlessness trade-off in AI, with potential developments focusing on improving safety mechanisms without stifling argumentative efficacy. Consequently, this work highlights how current guardrail implementations may require optimization to support tasks needing high safety and argumentative integrity, such as counterspeech generation in critical social contexts.