Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision
Philipp Christmann, Svitlana Vakulenko, Ionut Teodor Sorodoc, Bill Byrne, Adrià de Gispert
Abstract
Long-form question answering (LFQA) aims at generating in-depth answers to end-user questions, providing relevant information beyond the direct answer. However, existing retrievers are typically optimized towards information that directly targets the question, missing out on such contextual information. Furthermore, there is a lack of training data for relevant context. To this end, we propose and compare different weak supervision techniques to optimize retrieval for contextual information. Experiments demonstrate improvements on the end-to-end QA performance on ASQA, a dataset for long-form question answering. Importantly, as more contextual information is retrieved, we improve the relevant page recall for LFQA by 14.7% and the groundedness of generated long-form answers by 12.5%. Finally, we show that long-form answers often anticipate likely follow-up questions, via experiments on a conversational QA dataset.
BibTeX
@inproceedings{christmann-etal-2024-retrieving,
title = "Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision",
author = "Christmann, Philipp and
Vakulenko, Svitlana and
Sorodoc, Ionut Teodor and
Byrne, Bill and
de Gispert, Adri{\`a}",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.835/",
doi = "10.18653/v1/2024.findings-emnlp.835",
pages = "14301--14310"
}