# Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction

arXiv: https://arxiv.org/abs/2011.13137 · HTML: https://mlsanity.com/paper/2011.13137/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 19 of 19)

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- [Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2310.14696/) (2023)
- [Navigating Rifts in Human-LLM Grounding: Study and Benchmark](https://mlsanity.com/paper/2503.13975/) (2025)
- [Asking Clarification Questions to Handle Ambiguity in Open-Domain QA](https://mlsanity.com/paper/2305.13808/) (2023)
- [Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations](https://mlsanity.com/paper/2410.04241/) (2024)
- [Merging Facts, Crafting Fallacies: Evaluating the Contradictory Nature of Aggregated Factual Claims in Long-Form Generations](https://mlsanity.com/paper/2402.05629/) (2024)
- [Answering Ambiguous Questions via Iterative Prompting](https://mlsanity.com/paper/2307.03897/) (2023)
- [Open-Retrieval Conversational Machine Reading](https://mlsanity.com/paper/2102.08633/) (2021)
- [Answering Open-Domain Multi-Answer Questions via a Recall-then-Verify Framework](https://mlsanity.com/paper/2110.08544/) (2021)
- [Diversify-verify-adapt: Efficient and Robust Retrieval-Augmented Ambiguous Question Answering](https://mlsanity.com/paper/2409.02361/) (2024)
- [How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension](https://mlsanity.com/paper/2306.00435/) (2023)
- [Agentic Verification for Ambiguous Query Disambiguation](https://mlsanity.com/paper/2502.10352/) (2025)
- [Inter-Passage Verification for Multi-evidence Multi-answer QA](https://mlsanity.com/paper/2506.00425/) (2025)
- [Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation](https://mlsanity.com/paper/2605.25831/) (2026)
- [Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models](https://mlsanity.com/paper/2507.03726/) (2025)
- [A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries](https://mlsanity.com/paper/2608.19875/) (2026)

## Abstract (arXiv)

In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. Therefore, a system needs to find possible interpretations of the question, and predict one or multiple plausible answers. When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity. In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. In addition, we propose a novel round-trip prediction approach to iteratively generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output. Our model, named Refuel, achieves a new state-of-the-art performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA. The proposed round-trip prediction is a model-agnostic general approach for answering ambiguous open-domain questions, which improves our Refuel as well as several baseline models. We release source code for our models and experiments at https://github.com/amzn/refuel-open-domain-qa.