Explainable Verbal Reasoner Plus (EVR+)：一种支持多样组合推理的自然语言推理框架
Explainable Verbal Reasoner Plus (EVR+): A Natural Language Reasoning Framework that Supports Diverse Compositional Reasoning
AI总结：
针对语言模型组合泛化能力不足的问题，提出支持嵌套循环、多类型递归等多样推理的EVR+框架，通过显式生成执行符号算子、灵活分解任务提升组合推理能力，微调模型在5项合成任务上表现更优，还探讨了与少样本提示模型结合的可能与挑战。
摘要：
语言模型已成功应用于自然语言处理中的各类推理任务，但这类模型仍存在组合泛化能力不足的问题。本文提出Explainable Verbal Reasoner Plus（EVR+，可解释言语推理器增强版），这一推理框架通过两点提升语言模型的组合推理能力：（1）允许模型显式生成并执行符号算子；（2）允许模型以灵活的方式将复杂任务分解为若干更简单的子任务。与其前身Explainable Verbal Reasoner（EVR，可解释言语推理器）及其他采用类似思路的已有方法相比，本框架支持更多样的推理类型，例如嵌套循环和不同类型的递归。为评估该推理框架，我们构建了一个包含5项需组合推理的任务的合成数据集。结果表明，使用经过微调的语言模型时，本推理框架能提升语言模型在这5项任务上的组合泛化性能。我们还探讨了将本推理框架与少样本提示式语言模型相结合的可能性与挑战。
英文摘要：
Languages models have been successfully applied to a variety of reasoning tasks in NLP, yet the language models still suffer from compositional generalization. In this paper we present Explainable Verbal Reasoner Plus (EVR+), a reasoning framework that enhances language models' compositional reasoning ability by (1) allowing the model to explicitly generate and execute symbolic operators, and (2) allowing the model to decompose a complex task into several simpler ones in a flexible manner. Compared with its predecessor Explainable Verbal Reasoner (EVR) and other previous approaches adopting similar ideas, our framework supports more diverse types of reasoning such as nested loops and different types of recursion. To evaluate our reasoning framework, we build a synthetic dataset with five tasks that require compositional reasoning. Results show that our reasoning framework can enhance the language model's compositional generalization performance on the five tasks, using a fine-tuned language model. We also discussed the possibility and the challenges to combine our reasoning framework with a few-shot prompted language model.
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