Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910711934877696 |
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| author | Ramu, Pritika Goswami, Koustava Saxena, Apoorv Srinivasan, Balaji Vasan |
| author_facet | Ramu, Pritika Goswami, Koustava Saxena, Apoorv Srinivasan, Balaji Vasan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_17073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition Ramu, Pritika Goswami, Koustava Saxena, Apoorv Srinivasan, Balaji Vasan Computation and Language 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. |
| title | Enhancing Post-Hoc Attributions in Long Document Comprehension via Coarse Grained Answer Decomposition |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.17073 |