仅提示一次：论大语言模型上提示学习应对有毒内容的能力
You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content
AI总结：
本文研究利用大语言模型与提示学习处理有毒内容，在毒性分类、有毒片段检测和去毒化三项任务上取得优于或接近专用模型的性能，并显著降低毒性同时保持语义。
摘要：
有毒内容在网络上的传播是一个重要问题，对在线用户体验乃至整个社会都产生了不利影响。受该问题重要性和影响的驱动，研究致力于开发检测有毒内容的解决方案，通常利用在人工标注数据集上训练的机器学习（ML）模型。尽管这些努力很重要，但这些模型通常泛化能力不佳，无法应对新趋势（例如新有毒术语的出现）。当前，我们正见证应对在线社会问题方法的转变，特别是利用GPT-3或T5等大语言模型（LLMs），这些模型在海量语料上训练并具有强大的泛化能力。在这项工作中，我们研究如何利用LLMs和提示学习来解决有毒内容问题，特别聚焦于三项任务：1）毒性分类，2）有毒片段检测，以及3）去毒化。我们在五种模型架构和八个数据集上进行了广泛评估，证明采用提示学习的LLMs与在这些特定任务上训练的模型相比，可以达到相似甚至更好的性能。我们发现，与基线相比，提示学习在毒性分类任务上取得了约10%的提升，而在有毒片段检测任务上，我们发现其性能优于最佳基线（以F1分数计为0.643对0.640）。最后，对于去毒化任务，我们发现提示学习能够在保持语义含义的同时成功降低平均毒性分数（从0.775降至0.213）。
英文摘要：
The spread of toxic content online is an important problem that has adverse effects on user experience online and in our society at large. Motivated by the importance and impact of the problem, research focuses on developing solutions to detect toxic content, usually leveraging machine learning (ML) models trained on human-annotated datasets. While these efforts are important, these models usually do not generalize well and they can not cope with new trends (e.g., the emergence of new toxic terms). Currently, we are witnessing a shift in the approach to tackling societal issues online, particularly leveraging large language models (LLMs) like GPT-3 or T5 that are trained on vast corpora and have strong generalizability. In this work, we investigate how we can use LLMs and prompt learning to tackle the problem of toxic content, particularly focusing on three tasks; 1) Toxicity Classification, 2) Toxic Span Detection, and 3) Detoxification. We perform an extensive evaluation over five model architectures and eight datasets demonstrating that LLMs with prompt learning can achieve similar or even better performance compared to models trained on these specific tasks. We find that prompt learning achieves around 10\% improvement in the toxicity classification task compared to the baselines, while for the toxic span detection task we find better performance to the best baseline (0.643 vs. 0.640 in terms of $F_1$-score). Finally, for the detoxification task, we find that prompt learning can successfully reduce the average toxicity score (from 0.775 to 0.213) while preserving semantic meaning.
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。
