Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
Abstract
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.
Keywords
Cite
@article{arxiv.2310.14696,
title = {Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models},
author = {Gangwoo Kim and Sungdong Kim and Byeongguk Jeon and Joonsuk Park and Jaewoo Kang},
journal= {arXiv preprint arXiv:2310.14696},
year = {2023}
}
Comments
Accepted to EMNLP 2023