Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition
Pritika Ramu, Koustava Goswami, Apoorv Saxena, Balaji Vasan Srinivasan
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.- Anthology ID:
- 2024.emnlp-main.985
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 17790–17806
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.985/
- DOI:
- 10.18653/v1/2024.emnlp-main.985
- Cite (ACL):
- Pritika Ramu, Koustava Goswami, Apoorv Saxena, and Balaji Vasan Srinivasan. 2024. Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17790–17806, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition (Ramu et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.985.pdf