Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition

TL;DR

This paper introduces a novel method for improving post-hoc attribution in long document question answering by decomposing answers into information units using template-based in-context learning, enhancing attribution accuracy.

Contribution

It proposes a new approach for factual decomposition of answers with in-context learning, addressing granularity and attribution challenges in long document comprehension.

Findings

Improved attribution accuracy with answer decomposition.

Enhanced semantic understanding of answers.

Thorough comparison of attribution methods.

Abstract

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.