面向含不可答性的KBQA少样本迁移的弱验证器迭代修复方法
Iterative Repair with Weak Verifiers for Few-shot Transfer in KBQA with Unanswerability
AI总结：
该研究提出含不可答问题的KBQA少样本迁移新任务与两个评估数据集，提出结合强弱验证器反馈迭代修复的FUn-FuSIC方法，在该任务及可答类少样本迁移任务上均达到最优性能。
摘要：
知识库问答（KBQA）的实际应用要求模型在领域内标注训练数据有限的情况下处理不可答问题。我们提出了含不可答问题的KBQA少样本迁移这一新任务，并提供两个新数据集用于性能评估。我们提出FUn-FuSIC——针对该任务的新型解决方案，它扩展了FuSIC KBQA，后者是仅针对可答问题KBQA的当前最优少样本迁移模型。我们首先发现FuSIC-KBQA的迭代修复强假设所有问题均不可答。作为补救，我们提出不可答性反馈（FUn），该方法利用一组强验证器和弱验证器的反馈进行迭代修复，并适配了面向不可答性的自一致性机制，以更好地评估问题的可答性。我们的实验表明，FUn-FuSIC在我们的任务上显著优于多个基于大语言模型（LLM）的适配模型以及有监督的当前最优模型，同时也在可答类少样本迁移任务上建立了新的最优水平。
英文摘要：
Real-world applications of KBQA require models to handle unanswerable questions with a limited volume of in-domain labeled training data. We propose the novel task of few-shot transfer for KBQA with unanswerable questions and contribute two new datasets for performance evaluation. We present FUn-FuSIC - a novel solution for our task that extends FuSIC KBQA, the state-of-the-art few-shot transfer model for answerable-only KBQA. We first note that FuSIC-KBQA's iterative repair makes a strong assumption that all questions are unanswerable. As a remedy, we propose Feedback for Unanswerability (FUn), which uses iterative repair using feedback from a suite of strong and weak verifiers, and an adaptation of self consistency for unanswerabilty to better assess the answerability of a question. Our experiments show that FUn-FuSIC significantly outperforms suitable adaptations of multiple LLM based and supervised SoTA models on our task, while establishing a new SoTA for answerable few-shot transfer as well.
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