Recent advancements in Large Language Models (LLMs) have demonstrated exceptional capabilities in complex tasks like machine translation, commonsense reasoning, and language understanding. One of the primary reasons for the adaptability of LLMs in such diverse tasks is their in-context learning (ICL) capability, which allows them to perform well on new tasks by simply using a few task samples in the prompt. Despite their effectiveness in enhancing the performance of LLMs on diverse language and tabular tasks, these methods have not been thoroughly explored for their potential to generate post hoc explanations. In this work, we carry out one of the first explorations to analyze the effectiveness of LLMs in explaining other complex predictive models using ICL. To this end, we propose a novel framework, In-Context Explainers, comprising of three novel approaches that exploit the ICL capabilities of LLMs to explain the predictions made by other predictive models. We conduct extensive analysis with these approaches on real-world tabular and text datasets and demonstrate that LLMs are capable of explaining other predictive models similar to state-of-the-art post hoc explainers, opening up promising avenues for future research into LLM-based post hoc explanations of complex predictive models.

翻译：近年来，大语言模型（LLMs）在机器翻译、常识推理和语言理解等复杂任务中展现出卓越能力。LLMs能够适应如此多样化任务的主要原因之一是其上下文学习（ICL）能力，该能力使其仅需在提示中使用少量任务样本即可在新任务中表现良好。尽管这些方法在提升LLMs处理多样化语言和表格任务性能方面卓有成效，但其生成事后解释的潜力尚未得到充分探索。本研究率先探索了LLMs利用ICL解释其他复杂预测模型的有效性。为此，我们提出了一个名为"上下文解释器"的创新框架，包含三种新方法，这些方法利用LLMs的ICL能力来解释其他预测模型做出的预测。我们在真实世界的表格和文本数据集上对这些方法进行了广泛分析，结果表明LLMs能够以媲美最先进事后解释器的方式解释其他预测模型，这为未来基于LLM的复杂预测模型事后解释研究开辟了广阔前景。