# Model Analysis & Evaluation for Ambiguous Question Answering

Source: [Model Analysis & Evaluation for Ambiguous Question Answering](<https://www.sota2.com/research/paper/68fa6f641e209131cea4542d>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T17:04:04.627Z

Authors: Konstantinos Papakostas, Irene Papadopoulou

Published: 2023-05-21T15:20:20.000Z

## Abstract

Ambiguous questions are a challenge for Question Answering models, as they require answers that cover multiple interpretations of the original query. To this end, these models are required to generate long-form answers that often combine conflicting pieces of information. Although recent advances in the field have shown strong capabilities in generating fluent responses, certain research questions remain unanswered. Does model/data scaling improve the answers' quality? Do automated metrics align with human judgment? To what extent do these models ground their answers in evidence? In this study, we aim to thoroughly investigate these aspects, and provide valuable insights into the limitations of the current approaches. To aid in reproducibility and further extension of our work, we open-source our code at https://github.com/din0s/ambig\_lfqa.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.12483>)

[PDF](<https://arxiv.org/pdf/2305.12483.pdf>)