越安全越好吗？护栏对 LLM 在仇恨言论反制中论证强度的影响
Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLMs in Hate Speech Countering
AI总结：
本文研究 LLM 生成反制言论时安全护栏对论证质量的影响，发现护栏可能降低生成质量，而针对仇恨言论中隐含负面刻板印象和仇恨性部分进行攻击能产生更高质量的反制言论。
摘要：
反制言论作为仇恨言论缓解策略的潜在有效性正在引起自然语言生成（NLG）研究界越来越多的关注，尤其是自动生成反制言论的任务。然而，自动生成的回应往往缺乏专家撰写的反制言论所具有的论证丰富性。在这项工作中，我们聚焦于反制言论生成的两个方面，以产生更具说服力的回应。首先，通过考察 LLM 的有助性与无害性之间的张力，我们测试安全护栏的存在是否会阻碍生成质量。其次，我们评估针对仇恨言论的特定组成部分进行攻击是否会形成更有效的对抗网络仇恨的论证策略。通过进行广泛的人工和自动评估，我们表明安全护栏的存在也可能对一个本质上旨在促进积极社会互动的任务产生不利影响。此外，我们的结果表明，针对仇恨言论的特定组成部分进行攻击，尤其是其隐含的负面刻板印象和仇恨性部分，会带来更高质量的生成。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
