  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.
