TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Base (2022.emnlp-main)
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| Challenge: | KBQA is a challenging area for pre-trained language models due to its extensive space and complexity. |
| Approach: | They propose a model that uses multi-grained retrieval to focus on most relevant KB contexts . constrained decoding is used to control output space and reduce generation errors . |
| Outcome: | The proposed model outperforms existing models on GrailQA and WebQuestionsSP. |
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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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| Challenge: | Knowledge base question answering (KBQA) is a challenging task, particularly in parsing intricate questions into executable logical forms. |
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| Challenge: | Existing methods for Knowledge Base Question Answering generate non-executable queries and inefficiencies in query execution. |
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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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Shulin Cao, Jiaxin Shi, Liangming Pan, Lunyiu Nie, Yutong Xiang, Lei Hou, Juanzi Li, Bin He, Hanwang Zhang
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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 (KBQA) face hallucination problems, resulting in low accuracy. |
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| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
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| Challenge: | Recent advances in Large Language Models have led to low-level LFs that are limited to the knowledge of underlying LLM about the LF. |
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