Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
This addresses the challenge of handling multiple interpretations in ambiguous questions for open-domain QA systems, representing an incremental improvement over prior methods.
The paper tackles the problem of answering ambiguous open-domain questions by proposing the Tree of Clarifications framework, which recursively constructs a disambiguation tree using retrieval-augmented LLMs and outperforms baselines on ASQA, achieving higher Disambig-F1 and Disambig-ROUGE scores.
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.