# PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering

Source: [PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering](<https://www.sota2.com/research/paper/68fa712e1e209131cea4fcc1>)

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

Data updated: 2025-10-26T02:00:40.638Z

Authors: Wookje Han, Jinsol Park, Kyungjae Lee

Published: 2023-10-24T19:47:26.000Z

## Abstract

Information-seeking questions in long-form question answering (LFQA) often prove misleading due to ambiguity or false presupposition in the question. While many existing approaches handle misleading questions, they are tailored to limited questions, which are insufficient in a real-world setting with unpredictable input characteristics. In this work, we propose PreWoMe, a unified approach capable of handling any type of information-seeking question. The key idea of PreWoMe involves extracting presuppositions in the question and exploiting them as working memory to generate feedback and action about the question. Our experiment shows that PreWoMe is effective not only in tackling misleading questions but also in handling normal ones, thereby demonstrating the effectiveness of leveraging presuppositions, feedback, and action for real-world QA settings.

## 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/2310.16147>)

[PDF](<https://arxiv.org/pdf/2310.16147.pdf>)