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arXiv 2210.07652cs.CLcs.AI
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使分类器能够做出符合人类价值观的明确判断

Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values

The Hong Kong University of Science and Technology(香港科技大学);Meta AI

Yejin Bang, Tiezheng Yu, Andrea Madotto, Zhaojiang Lin, Mona Diab, Pascale Fung

摘要:

许多NLP分类任务,例如性别歧视/种族主义检测或毒性检测,都是基于人类价值观的。然而,人的价值观在不同的文化条件下会有所不同。因此,我们引入了一个值对齐分类的框架,该框架基于命令中显式写入的人类值执行预测。随着任务,我们提出了一个实用的方法,从大规模语言模型(LLM)中提取值对齐的知识,构建值对齐的分类器在两个步骤。首先,我们从LLM中通过基于样本的少样本学习生成值对齐的训练数据。接下来,我们使用为任务生成的数据微调较小的分类模型。实证结果表明,我们的VA模型在F1分数上超过多个基线至少15.56%,包括使用OPT-175 B和现有文本增强方法的少样本学习。我们认为,使用具有明确人类价值输入的分类器可以提高AI的包容性和可解释性。

英文摘要:

Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values. Yet, human values can vary under diverse cultural conditions. Therefore, we introduce a framework for value-aligned classification that performs prediction based on explicitly written human values in the command. Along with the task, we propose a practical approach that distills value-aligned knowledge from large-scale language models (LLMs) to construct value-aligned classifiers in two steps. First, we generate value-aligned training data from LLMs by prompt-based few-shot learning. Next, we fine-tune smaller classification models with the generated data for the task. Empirical results show that our VA-Models surpass multiple baselines by at least 15.56% on the F1-score, including few-shot learning with OPT-175B and existing text augmentation methods. We suggest that using classifiers with explicit human value input improves both inclusivity & explainability in AI.

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。

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