Accurately attributing answer text to its source document is crucial for developing a reliable question-answering system. However, attribution for long documents remains largely unexplored. Post-hoc attribution systems are designed to map answer text back to the source document, yet the granularity of this mapping has not been addressed. Furthermore, a critical question arises: What exactly should be attributed? This involves identifying the specific information units within an answer that require grounding. In this paper, we propose and investigate a novel approach to the factual decomposition of generated answers for attribution, employing template-based in-context learning. To accomplish this, we utilize the question and integrate negative sampling during few-shot in-context learning for decomposition. This approach enhances the semantic understanding of both abstractive and extractive answers. We examine the impact of answer decomposition by providing a thorough examination of various attribution approaches, ranging from retrieval-based techniques to LLM-based attributors.
@article{arxiv.2409.17073,
title = {Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition},
author = {Pritika Ramu and Koustava Goswami and Apoorv Saxena and Balaji Vasan Srinivasan},
journal= {arXiv preprint arXiv:2409.17073},
year = {2024}
}