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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Zonglin Yang",
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  "author": "Zonglin Yang",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "citation": [],
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2212.10923",
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  "defined_in": "https://doi.org/10.48550/arXiv.2212.10923",
            
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  "doi": "10.57702/h5kzw4gl",
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  "doi": "10.57702/h5kzw4gl",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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  "doi_publisher": "TIB",
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Li Dong",
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      "extra_author": "Li Dong",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Xinya Du",
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      "extra_author": "Xinya Du",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Hao Cheng",
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      "extra_author": "Hao Cheng",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Erik Cambria",
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      "extra_author": "Erik Cambria",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Xiaodong Liu",
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      "extra_author": "Xiaodong Liu",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Jianfeng Gao",
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      "extra_author": "Jianfeng Gao",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Furu Wei",
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      "extra_author": "Furu Wei",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    }
            
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  ],
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Natural Language Processing",
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      "display_name": "Natural Language Processing",
            
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      "name": "natural-language-processing",
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      "name": "natural-language-processing",
            
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      "title": "Natural Language Processing"
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      "title": "Natural Language Processing"
            
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    }
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    }
            
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  ],
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  ],
            
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  "license_title": null,
            
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  "metadata_created": "2024-12-16T19:38:21.379569",
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  "metadata_created": "2024-12-16T19:38:21.379569",
            
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  "metadata_modified": "2024-12-16T19:38:21.724413",
            
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  "name": "language-models-as-inductive-reasoners",
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  "name": "language-models-as-inductive-reasoners",
            
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  "notes": "Inductive reasoning is a core component of human 
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  "notes": "Inductive reasoning is a core component of human 
            
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intelligence. In the past research of inductive reasoning within 
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intelligence. In the past research of inductive reasoning within 
            
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computer science, logic language is used as representations of 
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computer science, logic language is used as representations of 
            
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knowledge (facts and rules, more specifically). However, logic 
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knowledge (facts and rules, more specifically). However, logic 
            
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language can cause systematic problems for inductive reasoning such as 
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language can cause systematic problems for inductive reasoning such as 
            
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disability of handling raw input such as natural language, 
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disability of handling raw input such as natural language, 
            
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sensitiveness to mislabeled data, and inca-pacity to handle ambiguous 
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sensitiveness to mislabeled data, and inca-pacity to handle ambiguous 
            
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input. To this end, we propose a new task, which is to induce natural 
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input. To this end, we propose a new task, which is to induce natural 
            
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language rules from natural language facts, and create a dataset 
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language rules from natural language facts, and create a dataset 
            
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termed DEER containing 1.2k rule-fact pairs for the task, where rules 
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termed DEER containing 1.2k rule-fact pairs for the task, where rules 
            
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and facts are written in natural language.",
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and facts are written in natural language.",
            
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      "created": "2024-12-16T18:25:42",
            
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distributions based on DCAT.",
            
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      "format": "JSON",
            
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  "tags": [
            
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    {
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    {
            
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      "display_name": "Inductive Reasoning",
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      "display_name": "Inductive Reasoning",
            
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      "name": "Inductive Reasoning",
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      "name": "Inductive Reasoning",
            
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    {
            
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      "display_name": "Natural Language Facts",
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      "display_name": "Natural Language Facts",
            
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      "id": "a87a0504-66be-430f-9825-d52c7f24cac4",
            
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      "name": "Natural Language Facts",
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      "name": "Natural Language Facts",
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Natural Language Rules",
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      "display_name": "Natural Language Rules",
            
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      "id": "a94637cc-3e61-4254-9c11-805b900947ce",
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      "id": "a94637cc-3e61-4254-9c11-805b900947ce",
            
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      "name": "Natural Language Rules",
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      "name": "Natural Language Rules",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Language Models as Inductive Reasoners",
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  "title": "Language Models as Inductive Reasoners",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}