Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models

• URL: http://arxiv.org/abs/2310.14696v1
• Date: Mon, 23 Oct 2023 08:42:49 GMT
• Title: Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
• Authors: Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, Joonsuk Park, Jaewoo Kang
• Abstract summary: Tree of Clarifications (ToC) is a framework to generate a long-form answer to ambiguous questions. ToC outperforms existing baselines on ASQA in a few-shot setup across the metrics.
• Score: 30.186503757127188
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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