Despite recent concerns about undesirable behaviors generated by large language models (LLMs), including non-factual, biased, and hateful language, we find LLMs are inherent multi-task language checkers based on their latent representations of natural and social knowledge. We present an interpretable, unified, language checking (UniLC) method for both human and machine-generated language that aims to check if language input is factual and fair. While fairness and fact-checking tasks have been handled separately with dedicated models, we find that LLMs can achieve high performance on a combination of fact-checking, stereotype detection, and hate speech detection tasks with a simple, few-shot, unified set of prompts. With the ``1/2-shot'' multi-task language checking method proposed in this work, the GPT3.5-turbo model outperforms fully supervised baselines on several language tasks. The simple approach and results suggest that based on strong latent knowledge representations, an LLM can be an adaptive and explainable tool for detecting misinformation, stereotypes, and hate speech.

该研究表明，大型语言模型(LMMs)基于其对自然和社会知识的潜在表示，具有多任务语言检查器的特性且可以执行事实检查、刻板印象检测和仇恨言论检测任务。通过“1/2-shot”多任务语言检查方法，研究者们提出了一种简单的方法，并在几个语言任务上优于全监督基线，这表明在强大的潜在知识表示的基础上，LLMs可以是检测错误信息、刻板印象和仇恨言论的自适应和可解释工具。

可解释的统一语言检查