In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. An alternative is to gather candidate answers and aggregate them, but this method can be computationally costly and may neglect dependencies among answers. In this paper, we present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions. Specifically, we integrate an answering model with a prompting model in an iterative manner. The prompting model adaptively tracks the reading process and progressively triggers the answering model to compose distinct and relevant answers. Additionally, we develop a task-specific post-pretraining approach for both the answering model and the prompting model, which greatly improves the performance of our framework. Empirical studies on two commonly-used open benchmarks show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. Additionally, AmbigPrompt also performs well in low-resource settings. The code are available at: https://github.com/sunnweiwei/AmbigPrompt.

翻译：在开放域问答中，由于问题的模糊性，可能存在多个合理的答案。为了为模糊问题提供可行的答案，一种方法是直接预测所有有效答案，但这可能难以平衡相关性和多样性。另一种替代方案是收集候选答案并进行聚合，但这种方法计算成本较高，且可能忽略答案之间的依赖关系。本文提出了AmbigPrompt，以解决现有方法在回答模糊问题中的不足。具体而言，我们将回答模型与提示模型以迭代方式集成。提示模型自适应地跟踪阅读过程，并逐步触发回答模型生成不同且相关的答案。此外，我们为回答模型和提示模型开发了任务特定的后预训练方法，显著提升了框架的性能。在两个常用开放基准上的实证研究表明，AmbigPrompt在减少内存使用和降低推理延迟的同时，达到了最先进或具有竞争力的结果。此外，AmbigPrompt在低资源场景下也表现良好。代码可在https://github.com/sunnweiwei/AmbigPrompt获取。