# A novel interface for adversarial trivia question-writing

Source: [A novel interface for adversarial trivia question-writing](<https://www.sota2.com/research/paper/68fa72bc1e209131cea5843e>)

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

Data updated: 2025-10-26T02:33:49.815Z

Authors: Jason Liu

Published: 2024-03-12T02:37:24.000Z

## Abstract

A critical component when developing question-answering AIs is an adversarial dataset that challenges models to adapt to the complex syntax and reasoning underlying our natural language. Present techniques for procedurally generating adversarial texts are not robust enough for training on complex tasks such as answering multi-sentence trivia questions. We instead turn to human-generated data by introducing an interface for collecting adversarial human-written trivia questions. Our interface is aimed towards question writers and players of Quiz Bowl, a buzzer-based trivia competition where paragraph-long questions consist of a sequence of clues of decreasing difficulty. To incentivize usage, a suite of machine learning-based tools in our interface assist humans in writing questions that are more challenging to answer for Quiz Bowl players and computers alike. Not only does our interface gather training data for the groundbreaking Quiz Bowl AI project QANTA, but it is also a proof-of-concept of future adversarial data collection for question-answering systems. The results of performance-testing our interface with ten originally-composed questions indicate that, despite some flaws, our interface's novel question-writing features as well as its real-time exposure of useful responses from our machine models could facilitate and enhance the collection of adversarial questions.

## 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/2404.00011>)

[PDF](<https://arxiv.org/pdf/2404.00011.pdf>)