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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.00600 |
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| _version_ | 1866909410531475456 |
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| author | Abdallah, Abdelrahman Mozafari, Jamshid Piryani, Bhawna Abdelgwad, Mohammed M. Jatowt, Adam |
| author_facet | Abdallah, Abdelrahman Mozafari, Jamshid Piryani, Bhawna Abdelgwad, Mohammed M. Jatowt, Adam |
| contents | This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts. In contrast, DynRank introduces a dynamic prompting mechanism, leveraging a pre-trained question classification model that categorizes questions into fine-grained types. Based on these classifications, contextually relevant prompts are generated, enabling more effective passage retrieval. We integrate DynRank into existing retrieval frameworks and conduct extensive experiments on multiple QA benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00600 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification Abdallah, Abdelrahman Mozafari, Jamshid Piryani, Bhawna Abdelgwad, Mohammed M. Jatowt, Adam Computation and Language This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts. In contrast, DynRank introduces a dynamic prompting mechanism, leveraging a pre-trained question classification model that categorizes questions into fine-grained types. Based on these classifications, contextually relevant prompts are generated, enabling more effective passage retrieval. We integrate DynRank into existing retrieval frameworks and conduct extensive experiments on multiple QA benchmark datasets. |
| title | DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.00600 |