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DataScienceUIBK/ComplexTempQA|时间问答数据集|自然语言处理数据集
收藏hugging_face2024-09-22 更新2024-06-15 收录
下载链接：
https://hf-mirror.com/datasets/DataScienceUIBK/ComplexTempQA
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链接失效反馈资源简介：
ComplexTempQA是一个大规模的数据集，专为复杂的时间问题回答（TQA）设计。它包含超过1亿个问题-答案对，涵盖了1987年至2023年的事件、实体和时间段。问题分为属性、比较和计数三种主要类型，并进一步细分为与事件、实体或时间段相关的子类型。数据集还包含丰富的元数据，如唯一标识符、问题文本、答案、问题类型、难度评分、时间范围等。数据集可用于评估和训练大型语言模型的时间推理能力，支持时间问题回答、信息检索和语言理解的研究。
提供机构：
DataScienceUIBK
原始信息汇总
ComplexTempQA 数据集
ComplexTempQA 是一个大规模的复杂时间问答（TQA）数据集。它包含超过 1 亿个问答对，是 TQA 领域中最大的数据集之一。该数据集使用来自 Wikipedia 和 Wikidata 的数据生成，涵盖了 36 年的时间范围（1987-2023）。
数据集描述
ComplexTempQA 将问题分为三种主要类型：
- 属性问题
- 比较问题
- 计数问题
这些类别根据其与事件、实体或时间段的关联进一步细分。
问题类型和数量
| 问题类型 | 子类型 | 数量 |
|---|---|---|
| 属性 | 事件 | 83,798 |
| 属性 | 实体 | 84,079 |
| 属性 | 时间 | 9,454 |
| 比较 | 事件 | 25,353,340 |
| 比较 | 实体 | 74,678,117 |
| 比较 | 时间 | 54,022,952 |
| 计数 | 事件 | 18,325 |
| 计数 | 实体 | 10,798 |
| 计数 | 时间 | 12,732 |
| 多跳 | 76,933 | |
| 未命名事件 | 8,707,123 | |
| 总计 | 100,228,457 |
元数据
- id: 每个问题的唯一标识符。
- question: 问题的文本。
- answer: 问题的答案。
- type: 根据数据集分类法的问题类型。
- rating: 问题的难度评级（
0表示简单，1表示困难）。 - timeframe: 问题相关的时间范围。
- question_entity: 与问题中实体相关的 Wikidata ID 列表。
- answer_entity: 与答案中实体相关的 Wikidata ID 列表。
- question_country: 与问题中实体或事件相关的国家 Wikidata ID 列表。
- answer_country: 与答案中实体或事件相关的国家 Wikidata ID 列表。
- is_unnamed: 指示问题是否包含隐式描述的事件（
1表示是，0表示否）。
数据集特征
大小
ComplexTempQA 包含超过 1 亿个问答对，重点关注 1987 年至 2023 年间的事件、实体和时间段。
复杂性
问题需要高级推理技能，包括多跳问答、时间聚合和跨时间比较。
分类法
数据集遵循独特的分类法，将问题分为属性、比较和计数类型，确保全面覆盖时间查询。
评估
数据集已评估可读性、网络搜索前后的回答难易程度以及整体清晰度。人工评分员评估了部分问题，以确保高质量。
用途
评估和训练
ComplexTempQA 可用于：
- 评估大型语言模型（LLMs）的时间推理能力
- 微调语言模型以提高时间理解能力
- 开发和测试检索增强生成（RAG）系统
研究应用
数据集支持以下研究：
- 时间问答
- 信息检索
- 语言理解
适应和持续学习
ComplexTempQA 的时间元数据有助于开发在线适应和持续训练方法，促进时间基础学习和评估的探索。
AI搜集汇总
数据集介绍
构建方式
ComplexTempQA数据集的构建，是基于Wikipedia和Wikidata的海量数据，通过精心设计的问题-答案对形式，覆盖了从1987年至2023年间的历史事件、实体和时间跨度。该数据集通过三种主要问题类型——属性问题、比较问题和计数问题，进一步细分为与事件、实体或时间相关的子类型，形成了超过一亿的问题-答案对，旨在为复杂时间问题回答任务提供全面的数据支持。
特点
ComplexTempQA数据集以其庞大的规模、复杂的问题类型和详尽的元数据而显著。它不仅包含大量的问题-答案对，而且这些问题在复杂性上要求高级推理技能，包括多跳问题回答、时间聚合和跨时间比较。数据集采用独特的分类法，将问题分类为属性、比较和计数类型，确保了对时间查询的全面覆盖。此外，数据集经过人类评估，保证了高质量的问题和答案。
使用方法
用户可以通过访问数据集的GitHub页面来获取ComplexTempQA数据集和相应的代码。该数据集适用于评估大型语言模型的时序推理能力、微调语言模型以增强其时序理解能力，以及开发和测试检索增强生成系统。此外，其丰富的时序元数据为在线适应和持续训练语言模型提供了便利，有助于探索基于时间的学习和评估方法。
背景与挑战
背景概述
ComplexTempQA数据集，作为一项大规模的复杂时态问题回答（TQA）研究工具，诞生于对时间序列信息处理需求的深刻认识。该数据集由DataScienceUIBK团队开发，并在1987年至2023年的广阔时间跨度内，依托Wikipedia与Wikidata的海量数据，构建了包含超过一亿条问题-答案对。其旨在推动对事件、实体及时间周期相关问题的深入理解与应答，对自然语言处理领域产生了显著影响，特别是在时态理解与问题解答方面。
当前挑战
ComplexTempQA数据集面临的挑战主要在于其问题的复杂性与多样性，这要求模型具备高级推理能力，如多跳问答、时态聚合以及跨时间比较等。构建过程中，确保数据的准确性、问题的合理分布以及答案的相关性是另一大挑战。此外，如何高效利用数据集中的时态元数据，以支持在线适应与持续学习策略，对于提升大型语言模型在时态处理方面的能力至关重要。
常用场景
经典使用场景
在复杂时序问题回答的研究领域中，ComplexTempQA数据集以其庞大的规模和精细的类别划分，成为检验与训练大型语言模型时序推理能力的经典资源。研究者利用该数据集，对模型进行评估，以确定其在处理涉及事件、实体和时间跨度的复杂查询时的表现。
实际应用
实际应用中，ComplexTempQA数据集为信息检索、语言理解等领域提供了强有力的支持。通过该数据集，开发者能够构建更为智能的问答系统，服务于新闻分析、历史研究、事件追踪等多个场景，极大地提升了信息处理的效率和准确性。
衍生相关工作
ComplexTempQA数据集催生了一系列相关研究工作，包括但不限于对大型语言模型进行时序推理能力的评估、检索增强生成系统的开发，以及在线适应和持续训练方法的研究。这些工作进一步拓展了数据集的应用范围，为人工智能领域的发展贡献了重要力量。
以上内容由AI搜集并总结生成
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