Long-form question answering (LFQA) tasks require retrieving the documents pertinent to a query, using them to form a paragraph-length answer. Despite considerable progress in LFQA modeling, fundamental issues impede its progress: i) train/validation/test dataset overlap, ii) absence of automatic metrics and iii) generated answers not being "grounded" in retrieved documents. This work addresses every one these critical bottlenecks, contributing natural language inference/generation (NLI/NLG) methods and metrics that make significant strides to their alleviation.
@article{arxiv.2112.13432,
title = {New Methods & Metrics for LFQA tasks},
author = {Suchismit Mahapatra and Vladimir Blagojevic and Pablo Bertorello and Prasanna Kumar},
journal= {arXiv preprint arXiv:2112.13432},
year = {2021}
}