ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909205622947840 |
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| author | Yuan, Siyu Chen, Jiangjie Sun, Changzhi Liang, Jiaqing Xiao, Yanghua Yang, Deqing |
| author_facet | Yuan, Siyu Chen, Jiangjie Sun, Changzhi Liang, Jiaqing Xiao, Yanghua Yang, Deqing |
| contents | Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large language models (LLMs), followed by minor human efforts for data quality control. Evaluations on a series of datasets of two analogical reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB successfully enables both smaller LMs and LLMs to gain better analogical reasoning capabilities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_05994 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base Yuan, Siyu Chen, Jiangjie Sun, Changzhi Liang, Jiaqing Xiao, Yanghua Yang, Deqing Computation and Language Artificial Intelligence Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large language models (LLMs), followed by minor human efforts for data quality control. Evaluations on a series of datasets of two analogical reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB successfully enables both smaller LMs and LLMs to gain better analogical reasoning capabilities. |
| title | ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2305.05994 |