Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values

• URL: http://arxiv.org/abs/2210.07652v1
• Date: Fri, 14 Oct 2022 09:10:49 GMT
• Title: Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values
• Authors: Yejin Bang, Tiezheng Yu, Andrea Madotto, Zhaojiang Lin, Mona Diab, Pascale Fung
• Abstract summary: Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values. We introduce a framework for value-aligned classification that performs prediction based on explicitly written human values in the command.
• Score: 73.82043713141142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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