| Challenge: | Existing KBQA datasets are insufficient for numerical reasoning . existing KBqa datasets lack multi-hop reasoning and numerical reasoning. |
| Approach: | They propose a task that necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
| Outcome: | The proposed task necessitates the ability to perform multi-hop reasoning and numerical reasoning. |
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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning
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Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation (2025.emnlp-main)
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| Challenge: | Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. |
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Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models (2024.acl-long)
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| Challenge: | Knowledge base question answering (KBQA) is a challenging task, particularly in parsing intricate questions into executable logical forms. |
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A Two-Stage Approach towards Generalization in Knowledge Base Question Answering (2022.findings-emnlp)
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Srinivas Ravishankar, Dung Thai, Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Pavan Kapanipathi, Gaetano Rossiello, Achille Fokoue
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| Challenge: | Existing question answering datasets lack numerical reasoning and reasoning processes . current research on numerical reasoning focuses on simple calculations . |
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| Challenge: | Existing methods for question answering over knowledge bases (KBQA) suffer from generalization issues due to coarse-grained modeling of the logical expression. |
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| Challenge: | Existing methods for knowledge base question answering lack grammaticality, faithfulness, and controllability due to hallucinations in the reasoning process. |
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ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)
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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu
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