chansung/auto-paper-qa2

--- dataset_info: features: - name: title dtype: string - name: summary dtype: string - name: abstract dtype: string - name: authors dtype: string - name: arxiv_id dtype: string - name: target_date dtype: timestamp[s] - name: full_text dtype: string - name: 0_question dtype: string - name: 0_answers:eli5 dtype: string - name: 0_answers:expert dtype: string - name: 0_additional_depth_q:follow up question dtype: string - name: 0_additional_depth_q:answers:eli5 dtype: string - name: 0_additional_depth_q:answers:expert dtype: string - name: 0_additional_breath_q:follow up question dtype: string - name: 0_additional_breath_q:answers:eli5 dtype: string - name: 0_additional_breath_q:answers:expert dtype: string - name: 1_question dtype: string - name: 1_answers:eli5 dtype: string - name: 1_answers:expert dtype: string - name: 1_additional_depth_q:follow up question dtype: string - name: 1_additional_depth_q:answers:eli5 dtype: string - name: 1_additional_depth_q:answers:expert dtype: string - name: 1_additional_breath_q:follow up question dtype: string - name: 1_additional_breath_q:answers:eli5 dtype: string - name: 1_additional_breath_q:answers:expert dtype: string - name: 2_question dtype: string - name: 2_answers:eli5 dtype: string - name: 2_answers:expert dtype: string - name: 2_additional_depth_q:follow up question dtype: string - name: 2_additional_depth_q:answers:eli5 dtype: string - name: 2_additional_depth_q:answers:expert dtype: string - name: 2_additional_breath_q:follow up question dtype: string - name: 2_additional_breath_q:answers:eli5 dtype: string - name: 2_additional_breath_q:answers:expert dtype: string - name: 3_question dtype: string - name: 3_answers:eli5 dtype: string - name: 3_answers:expert dtype: string - name: 3_additional_depth_q:follow up question dtype: string - name: 3_additional_depth_q:answers:eli5 dtype: string - name: 3_additional_depth_q:answers:expert dtype: string - name: 3_additional_breath_q:follow up question dtype: string - name: 3_additional_breath_q:answers:eli5 dtype: string - name: 3_additional_breath_q:answers:expert dtype: string - name: 4_question dtype: string - name: 4_answers:eli5 dtype: string - name: 4_answers:expert dtype: string - name: 4_additional_depth_q:follow up question dtype: string - name: 4_additional_depth_q:answers:eli5 dtype: string - name: 4_additional_depth_q:answers:expert dtype: string - name: 4_additional_breath_q:follow up question dtype: string - name: 4_additional_breath_q:answers:eli5 dtype: string - name: 4_additional_breath_q:answers:expert dtype: string - name: 5_question dtype: string - name: 5_answers:eli5 dtype: string - name: 5_answers:expert dtype: string - name: 5_additional_depth_q:follow up question dtype: string - name: 5_additional_depth_q:answers:eli5 dtype: string - name: 5_additional_depth_q:answers:expert dtype: string - name: 5_additional_breath_q:follow up question dtype: string - name: 5_additional_breath_q:answers:eli5 dtype: string - name: 5_additional_breath_q:answers:expert dtype: string - name: 6_question dtype: string - name: 6_answers:eli5 dtype: string - name: 6_answers:expert dtype: string - name: 6_additional_depth_q:follow up question dtype: string - name: 6_additional_depth_q:answers:eli5 dtype: string - name: 6_additional_depth_q:answers:expert dtype: string - name: 6_additional_breath_q:follow up question dtype: string - name: 6_additional_breath_q:answers:eli5 dtype: string - name: 6_additional_breath_q:answers:expert dtype: string - name: 7_question dtype: string - name: 7_answers:eli5 dtype: string - name: 7_answers:expert dtype: string - name: 7_additional_depth_q:follow up question dtype: string - name: 7_additional_depth_q:answers:eli5 dtype: string - name: 7_additional_depth_q:answers:expert dtype: string - name: 7_additional_breath_q:follow up question dtype: string - name: 7_additional_breath_q:answers:eli5 dtype: string - name: 7_additional_breath_q:answers:expert dtype: string - name: 8_question dtype: string - name: 8_answers:eli5 dtype: string - name: 8_answers:expert dtype: string - name: 8_additional_depth_q:follow up question dtype: string - name: 8_additional_depth_q:answers:eli5 dtype: string - name: 8_additional_depth_q:answers:expert dtype: string - name: 8_additional_breath_q:follow up question dtype: string - name: 8_additional_breath_q:answers:eli5 dtype: string - name: 8_additional_breath_q:answers:expert dtype: string - name: 9_question dtype: string - name: 9_answers:eli5 dtype: string - name: 9_answers:expert dtype: string - name: 9_additional_depth_q:follow up question dtype: string - name: 9_additional_depth_q:answers:eli5 dtype: string - name: 9_additional_depth_q:answers:expert dtype: string - name: 9_additional_breath_q:follow up question dtype: string - name: 9_additional_breath_q:answers:eli5 dtype: string - name: 9_additional_breath_q:answers:expert dtype: string - name: 10_question dtype: string - name: 10_answers:eli5 dtype: string - name: 10_answers:expert dtype: string - name: 10_additional_depth_q:follow up question dtype: string - name: 10_additional_depth_q:answers:eli5 dtype: string - name: 10_additional_depth_q:answers:expert dtype: string - name: 10_additional_breath_q:follow up question dtype: string - name: 10_additional_breath_q:answers:eli5 dtype: string - name: 10_additional_breath_q:answers:expert dtype: string - name: 11_question dtype: string - name: 11_answers:eli5 dtype: string - name: 11_answers:expert dtype: string - name: 11_additional_depth_q:follow up question dtype: string - name: 11_additional_depth_q:answers:eli5 dtype: string - name: 11_additional_depth_q:answers:expert dtype: string - name: 11_additional_breath_q:follow up question dtype: string - name: 11_additional_breath_q:answers:eli5 dtype: string - name: 11_additional_breath_q:answers:expert dtype: string - name: 12_question dtype: string - name: 12_answers:eli5 dtype: string - name: 12_answers:expert dtype: string - name: 12_additional_depth_q:follow up question dtype: string - name: 12_additional_depth_q:answers:eli5 dtype: string - name: 12_additional_depth_q:answers:expert dtype: string - name: 12_additional_breath_q:follow up question dtype: string - name: 12_additional_breath_q:answers:eli5 dtype: string - name: 12_additional_breath_q:answers:expert dtype: string - name: 2_additional_depth_q:answers:eli5:What are the major benefits of using ReFT? dtype: string - name: 2_additional_depth_q:answers:eli5:Why does the performance of ReFT suffer when it is applied to MathQAMCQ dataset? dtype: string - name: 2_additional_depth_q:answers:eli5:What are the components of the ReFT model and how do they work together? dtype: string - name: 2_additional_depth_q:answers:eli5:How did the researchers address the issue of reward hacking in the MathQAMCQ dataset? dtype: string - name: 2_additional_depth_q:answers:eli5:What are the advantages of using the P-CoT approach over the N-CoT approach? dtype: string - name: 2_additional_depth_q:answers:expert:What are the major benefits of using ReFT? dtype: string - name: 2_additional_depth_q:answers:expert:Why does the performance of ReFT suffer when it is applied to MathQAMCQ dataset? dtype: string - name: 2_additional_depth_q:answers:expert:What are the components of the ReFT model and how do they work together? dtype: string - name: 2_additional_depth_q:answers:expert:How did the researchers address the issue of reward hacking in the MathQAMCQ dataset? dtype: string - name: 2_additional_depth_q:answers:expert:What are the advantages of using the P-CoT approach over the N-CoT approach? dtype: string splits: - name: train num_bytes: 95278860 num_examples: 629 download_size: 50402234 dataset_size: 95278860 configs: - config_name: default data_files: - split: train path: data/train-* ---The dataset is structured around academic papers and their related questions and answers. It includes various fields such as title, summary, abstract, authors, arxiv_id, target_date, and full_text of the papers. Additionally, it contains multiple question and answer pairs categorized by indices (0 to 12) and types (eli5, expert, additional depth questions, and additional breadth questions). Each question type has corresponding answers. The dataset is split into a training set with 629 examples.

原始信息汇总

数据集概述

数据特征

数据集包含以下特征：

• title: 字符串类型
• summary: 字符串类型
• abstract: 字符串类型
• authors: 字符串类型
• arxiv_id: 字符串类型
• target_date: 时间戳类型
• full_text: 字符串类型
• 0_question: 字符串类型
• 0_answers:eli5: 字符串类型
• 0_answers:expert: 字符串类型
• 0_additional_depth_q:follow up question: 字符串类型
• 0_additional_depth_q:answers:eli5: 字符串类型
• 0_additional_depth_q:answers:expert: 字符串类型
• 0_additional_breath_q:follow up question: 字符串类型
• 0_additional_breath_q:answers:eli5: 字符串类型
• 0_additional_breath_q:answers:expert: 字符串类型
• 1_question: 字符串类型
• 1_answers:eli5: 字符串类型
• 1_answers:expert: 字符串类型
• 1_additional_depth_q:follow up question: 字符串类型
• 1_additional_depth_q:answers:eli5: 字符串类型
• 1_additional_depth_q:answers:expert: 字符串类型
• 1_additional_breath_q:follow up question: 字符串类型
• 1_additional_breath_q:answers:eli5: 字符串类型
• 1_additional_breath_q:answers:expert: 字符串类型
• 2_question: 字符串类型
• 2_answers:eli5: 字符串类型
• 2_answers:expert: 字符串类型
• 2_additional_depth_q:follow up question: 字符串类型
• 2_additional_depth_q:answers:eli5: 字符串类型
• 2_additional_depth_q:answers:expert: 字符串类型
• 2_additional_breath_q:follow up question: 字符串类型
• 2_additional_breath_q:answers:eli5: 字符串类型
• 2_additional_breath_q:answers:expert: 字符串类型
• 3_question: 字符串类型
• 3_answers:eli5: 字符串类型
• 3_answers:expert: 字符串类型
• 3_additional_depth_q:follow up question: 字符串类型
• 3_additional_depth_q:answers:eli5: 字符串类型
• 3_additional_depth_q:answers:expert: 字符串类型
• 3_additional_breath_q:follow up question: 字符串类型
• 3_additional_breath_q:answers:eli5: 字符串类型
• 3_additional_breath_q:answers:expert: 字符串类型
• 4_question: 字符串类型
• 4_answers:eli5: 字符串类型
• 4_answers:expert: 字符串类型
• 4_additional_depth_q:follow up question: 字符串类型
• 4_additional_depth_q:answers:eli5: 字符串类型
• 4_additional_depth_q:answers:expert: 字符串类型
• 4_additional_breath_q:follow up question: 字符串类型
• 4_additional_breath_q:answers:eli5: 字符串类型
• 4_additional_breath_q:answers:expert: 字符串类型
• 5_question: 字符串类型
• 5_answers:eli5: 字符串类型
• 5_answers:expert: 字符串类型
• 5_additional_depth_q:follow up question: 字符串类型
• 5_additional_depth_q:answers:eli5: 字符串类型
• 5_additional_depth_q:answers:expert: 字符串类型
• 5_additional_breath_q:follow up question: 字符串类型
• 5_additional_breath_q:answers:eli5: 字符串类型
• 5_additional_breath_q:answers:expert: 字符串类型
• 6_question: 字符串类型
• 6_answers:eli5: 字符串类型
• 6_answers:expert: 字符串类型
• 6_additional_depth_q:follow up question: 字符串类型
• 6_additional_depth_q:answers:eli5: 字符串类型
• 6_additional_depth_q:answers:expert: 字符串类型
• 6_additional_breath_q:follow up question: 字符串类型
• 6_additional_breath_q:answers:eli5: 字符串类型
• 6_additional_breath_q:answers:expert: 字符串类型
• 7_question: 字符串类型
• 7_answers:eli5: 字符串类型
• 7_answers:expert: 字符串类型
• 7_additional_depth_q:follow up question: 字符串类型
• 7_additional_depth_q:answers:eli5: 字符串类型
• 7_additional_depth_q:answers:expert: 字符串类型
• 7_additional_breath_q:follow up question: 字符串类型
• 7_additional_breath_q:answers:eli5: 字符串类型
• 7_additional_breath_q:answers:expert: 字符串类型
• 8_question: 字符串类型
• 8_answers:eli5: 字符串类型
• 8_answers:expert: 字符串类型
• 8_additional_depth_q:follow up question: 字符串类型
• 8_additional_depth_q:answers:eli5: 字符串类型
• 8_additional_depth_q:answers:expert: 字符串类型
• 8_additional_breath_q:follow up question: 字符串类型
• 8_additional_breath_q:answers:eli5: 字符串类型
• 8_additional_breath_q:answers:expert: 字符串类型
• 9_question: 字符串类型
• 9_answers:eli5: 字符串类型
• 9_answers:expert: 字符串类型
• 9_additional_depth_q:follow up question: 字符串类型
• 9_additional_depth_q:answers:eli5: 字符串类型
• 9_additional_depth_q:answers:expert: 字符串类型
• 9_additional_breath_q:follow up question: 字符串类型
• 9_additional_breath_q:answers:eli5: 字符串类型
• 9_additional_breath_q:answers:expert: 字符串类型
• 10_question: 字符串类型
• 10_answers:eli5: 字符串类型
• 10_answers:expert: 字符串类型
• 10_additional_depth_q:follow up question: 字符串类型
• 10_additional_depth_q:answers:eli5: 字符串类型
• 10_additional_depth_q:answers:expert: 字符串类型
• 10_additional_breath_q:follow up question: 字符串类型
• 10_additional_breath_q:answers:eli5: 字符串类型
• 10_additional_breath_q:answers:expert: 字符串类型
• 11_question: 字符串类型
• 11_answers:eli5: 字符串类型
• 11_answers:expert: 字符串类型
• 11_additional_depth_q:follow up question: 字符串类型
• 11_additional_depth_q:answers:eli5: 字符串类型
• 11_additional_depth_q:answers:expert: 字符串类型
• 11_additional_breath_q:follow up question: 字符串类型
• 11_additional_breath_q:answers:eli5: 字符串类型
• 11_additional_breath_q:answers:expert: 字符串类型
• 12_question: 字符串类型
• 12_answers:eli5: 字符串类型
• 12_answers:expert: 字符串类型
• 12_additional_depth_q:follow up question: 字符串类型
• 12_additional_depth_q:answers:eli5: 字符串类型
• 12_additional_depth_q:answers:expert: 字符串类型
• 12_additional_breath_q:follow up question: 字符串类型
• 12_additional_breath_q:answers:eli5: 字符串类型
• 12_additional_breath_q:answers:expert: 字符串类型
• 2_additional_depth_q:answers:eli5:What are the major benefits of using ReFT?: 字符串类型
• 2_additional_depth_q:answers:eli5:Why does the performance of ReFT suffer when it is applied to MathQAMCQ dataset?: 字符串类型
• 2_additional_depth_q:answers:eli5:What are the components of the ReFT model and how do they work together?: 字符串类型
• 2_additional_depth_q:answers:eli5:How did the researchers address the issue of reward hacking in the MathQAMCQ dataset?: 字符串类型
• 2_additional_depth_q:answers:eli5:What are the advantages of using the P-CoT approach over the N-CoT approach?: 字符串类型
• 2_additional_depth_q:answers:expert:What are the major benefits of using ReFT?: 字符串类型
• 2_additional_depth_q:answers:expert:Why does the performance of ReFT suffer when it is applied to MathQAMCQ dataset?: 字符串类型
• 2_additional_depth_q:answers:expert:What are the components of the ReFT model and how do they work together?: 字符串类型
• 2_additional_depth_q:answers:expert:How did the researchers address the issue of reward hacking in the MathQAMCQ dataset?: 字符串类型
• 2_additional_depth_q:answers:expert:What are the advantages of using the P-CoT approach over the N-CoT approach?: 字符串类型

数据分割

• train: 包含629个样本，占用95278860字节

数据集大小

• 下载大小: 50402234字节
• 数据集大小: 95278860字节

配置

• config_name: default
• data_files:
  • split: train
  • path: data/train-*