# A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering

Source: [A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering](<https://www.sota2.com/research/paper/68fa6e221e209131cea3e08d>)

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

Data updated: 2025-10-26T00:50:11.375Z

Authors: Matthew Maufe, James Ravenscroft, Rob Procter, Maria Liakata

Published: 2022-11-30T13:24:30.000Z

## Abstract

Question Answering (QA) is a growing area of research, often used to facilitate the extraction of information from within documents. State-of-the-art QA models are usually pre-trained on domain-general corpora like Wikipedia and thus tend to struggle on out-of-domain documents without fine-tuning. We demonstrate that synthetic domain-specific datasets can be generated easily using domain-general models, while still providing significant improvements to QA performance. We present two new tools for this task: A flexible pipeline for validating the synthetic QA data and training downstream models on it, and an online interface to facilitate human annotation of this generated data. Using this interface, crowdworkers labelled 1117 synthetic QA pairs, which we then used to fine-tune downstream models and improve domain-specific QA performance by 8.75 F1.

## 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/2211.16971>)

[PDF](<https://arxiv.org/pdf/2211.16971.pdf>)