Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
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arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908501890039808 |
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| author | Chen, Yongrui He, Junhao Fu, Linbo Zhang, Shenyu Jin, Rihui Dai, Xinbang Li, Jiaqi Min, Dehai Hu, Nan Zhang, Yuxin Qi, Guilin Huang, Yi Wu, Tongtong |
| author_facet | Chen, Yongrui He, Junhao Fu, Linbo Zhang, Shenyu Jin, Rihui Dai, Xinbang Li, Jiaqi Min, Dehai Hu, Nan Zhang, Yuxin Qi, Guilin Huang, Yi Wu, Tongtong |
| contents | Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way. Existing USKR methods rely on task-specific strategies or bespoke representations, which hinder their ability to dismantle barriers between different SKR tasks, thereby constraining their overall performance in cross-task scenarios. In this paper, we introduce \textsc{Pandora}, a novel USKR framework that addresses the limitations of existing methods by leveraging two key innovations. First, we propose a code-based unified knowledge representation using \textsc{Python}'s \textsc{Pandas} API, which aligns seamlessly with the pre-training of LLMs. This representation facilitates a cohesive approach to handling different structured knowledge sources. Building on this foundation, we employ knowledge transfer to bolster the unified reasoning process of LLMs by automatically building cross-task memory. By adaptively correcting reasoning using feedback from code execution, \textsc{Pandora} showcases impressive unified reasoning capabilities. Extensive experiments on six widely used benchmarks across three SKR tasks demonstrate that \textsc{Pandora} outperforms existing unified reasoning frameworks and competes effectively with task-specific methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17905 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning Chen, Yongrui He, Junhao Fu, Linbo Zhang, Shenyu Jin, Rihui Dai, Xinbang Li, Jiaqi Min, Dehai Hu, Nan Zhang, Yuxin Qi, Guilin Huang, Yi Wu, Tongtong Computation and Language Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way. Existing USKR methods rely on task-specific strategies or bespoke representations, which hinder their ability to dismantle barriers between different SKR tasks, thereby constraining their overall performance in cross-task scenarios. In this paper, we introduce \textsc{Pandora}, a novel USKR framework that addresses the limitations of existing methods by leveraging two key innovations. First, we propose a code-based unified knowledge representation using \textsc{Python}'s \textsc{Pandas} API, which aligns seamlessly with the pre-training of LLMs. This representation facilitates a cohesive approach to handling different structured knowledge sources. Building on this foundation, we employ knowledge transfer to bolster the unified reasoning process of LLMs by automatically building cross-task memory. By adaptively correcting reasoning using feedback from code execution, \textsc{Pandora} showcases impressive unified reasoning capabilities. Extensive experiments on six widely used benchmarks across three SKR tasks demonstrate that \textsc{Pandora} outperforms existing unified reasoning frameworks and competes effectively with task-specific methods. |
| title | Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2508.17905 |