Tree of Clarifications: Answering Ambiguous Questions with
  Retrieval-Augmented Large Language Models
Gangwoo Kim Sungdong Kim Byeongguk Jeon Joonsuk Park Jaewoo Kang
Questions in open-domain question answering are often ambiguous, allowing
multiple interpretations. One approach to handling them is to identify all
possible interpretations of the ambiguous question (AQ) and to generate a
long-form answer addressing them all, as suggested by Stelmakh et al., (2022).
While it provides a comprehensive response without bothering the user for
clarification, considering multiple dimensions of ambiguity and gathering
corresponding knowledge remains a challenge. To cope with the challenge, we
propose a novel framework, Tree of Clarifications (ToC): It recursively
constructs a tree of disambiguations for the AQ -- via few-shot prompting
leveraging external knowledge -- and uses it to generate a long-form answer.
ToC outperforms existing baselines on ASQA in a few-shot setup across the
metrics, while surpassing fully-supervised baselines trained on the whole
training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at
https://github.com/gankim/tree-of-clarifications.