Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition
<|reference_start|>Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition: 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.<|reference_end|>
@article{ramu2024enhancing,
    title={Enhancing Post-Hoc Attributions in Long Document Comprehension via
  Coarse Grained Answer Decomposition},
    author={Pritika Ramu, Koustava Goswami, Apoorv Saxena, Balaji Vasan Srinivasan},
    journal={arXiv preprint arXiv:2409.17073},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17073},
    primaryClass={cs.CL}
}