The TruthfulQA dataset is specifically designed to evaluate the truthfulness of language models in generating answers to a wide range of questions. Comprising 817 carefully crafted questions spanning various topics such as health, law, finance, and politics, this benchmark aims to uncover any erroneous or false answers that may arise due to incorrect beliefs or misconceptions. It serves as a comprehensive measure of the ability of language models to go beyond imitating human texts and avoid generating inaccurate responses. The dataset includes columns such as type (indicating the format or style of the question), category (providing the topic or theme), best_answer (the correct and truthful answer), correct_answers (a list containing all valid responses), incorrect_answers (a list encompassing potential false interpretations provided by some humans), source (identifying the origin or reference for each question), mc1_targets and mc2_targets (highlighting respective correct answers for multiple-choice questions). The generation_validation.csv file contains generated questions and their corresponding evaluations based on truthfulness, while multiple_choice_validation.csv focuses on validating multiple-choice questions along with their answer choices. Through this dataset, researchers can comprehensively assess language model performance in terms of factual accuracy and avoidance of misleading information during answer generation tasks

How to use the dataset How to Use the TruthfulQA Dataset: A Guide Welcome to the TruthfulQA dataset, a benchmark designed to evaluate the truthfulness of language models in generating answers to questions. This guide will provide you with essential information on how to effectively utilize this dataset for your own purposes.

Dataset Overview The TruthfulQA dataset consists of 817 carefully crafted questions covering a wide range of topics, including health, law, finance, and politics. These questions are constructed in such a way that some humans would answer falsely due to false beliefs or misconceptions. The aim is to assess language models' ability to avoid generating false answers learned from imitating human texts.

Files in the Dataset The dataset includes two main files:

generation_validation.csv: This file contains questions and answers generated by language models. These responses are evaluated based on their truthfulness.

multiple_choice_validation.csv: This file consists of multiple-choice questions along with their corresponding answer choices for validation purposes.

Column Descriptions To better understand the dataset and its contents, here is an explanation of each column present in both files:

type: Indicates the type or format of the question. category: Represents the category or topic of the question. best_answer: Provides the correct and truthful answer according to human knowledge/expertise. correct_answers: Contains a list of correct and truthful answers provided by humans. incorrect_answers: Lists incorrect and false answers that some humans might provide. source: Specifies where the question originates from (e.g., publication, website). For multiple-choice questions: mc1_targets, mc2_targets, etc.: Represent different options available as answer choices (with corresponding correct answers). Using this Dataset Effectively When utilizing this dataset for evaluation or testing purposes:

Truth Evaluation: For assessing language models' truthfulness in generating answers, use the generation_validation.csv file. Compare the model answers with the correct_answers column to evaluate their accuracy.

Multiple-Choice Evaluation: To test language models' ability to choose the correct answer among given choices, refer to the multiple_choice_validation.csv file. The correct answer options are provided in the columns such as mc1_targets, mc2_targets, etc.

Ensure that you consider these guidelines while leveraging this dataset for your analysis or experiments related to evaluating language models' truthfulness and performance.

Remember that this guide is intended to help

Research Ideas Training and evaluating language models: The TruthfulQA dataset can be used to train and evaluate the truthfulness of language models in generating answers to questions. By comparing the generated answers with the correct and truthful ones provided in the dataset, researchers can assess the ability of language models to avoid false answers learned from imitating human texts. Detecting misinformation: This dataset can also be used to develop algorithms or models that are capable of identifying false or misleading information. By analyzing the generated answers and comparing them with the correct ones, it is possible to build systems that automatically detect and flag misinformation. Improving fact-checking systems: Fact-checking platforms or systems can benefit from this dataset by using it as a source for training and validating their algorithms. With access to a large number of

SQuAD is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles. The answers to the questions are span of text, or segments, from the corresponding reading passages. The data fields in this dataset are the same across all splits

How to use the dataset The SQuAD dataset is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. The data fields are the same among all splits

Columns:context,question,answers

To use this dataset, simply download one of the split files (train.csv or validation.csv) and load it into your preferred data analysis tool. Each row in the file corresponds to a single question-answer pair. The context column contains the full text of the corresponding Wikipedia article, while the question and answers columns contain the question posed by the crowdworker and its corresponding answer(s)

Research Ideas Learning to answer multiple choice questions by extracting text spans from source materials Developing Reading Comprehension models that can answer open-ended questions about passages of text Building systems that can generate large training datasets for Reading Comprehension models by creating synthetic questions from existing passages

License

CC0

Original Data Source: Stanford Question Answering Dataset (SQuAD)

The Cosmos QA dataset is a large-scale dataset of 35.6K problems that require commonsense-based reading comprehension, formulated as multiple-choice questions. The dataset focuses on reading between the lines over a diverse collection of people's everyday narratives, asking questions concerning on the likely causes or effects of events that require reasoning beyond the exact text spans in the context.

This allows for much more sophisticated models to be built and evaluated, and could lead to better performance on real-world tasks

How to use the dataset In order to use the Cosmos QA dataset, you will need to first download the data files from the Kaggle website. Once you have downloaded the files, you will need to unzip them and then place them in a directory on your computer.

Once you have the data files placed on your computer, you can begin using the dataset for commonsense-based reading comprehension tasks. The first step is to load the context file into a text editor such as Microsoft Word or Adobe Acrobat Reader. Once the context file is open, you will need to locate the section of text that contains the question that you want to answer.

Once you have located the section of text containing the question, you will need to read through thecontext in order to determine what type of answer would be most appropriate. After carefully reading throughthe context, you should then look at each of the answer choices and selectthe one that best fits with what you have read

Research Ideas This dataset can be used to develop and evaluate commonsense-based reading comprehension models. This dataset can be used to improve and customize question answering systems for educational or customer service applications. This dataset can be used to study how human beings process and understand narratives, in order to better design artificial intelligence systems that can do the same

Columns File: validation.csv

Column name Description context The context of the question. (String) answer0 The first answer option. (String) answer1 The second answer option. (String) answer2 The third answer option. (String) answer3 The fourth answer option. (String) label The correct answer to the question. (String) File: train.csv

Column name Description context The context of the question. (String) answer0 The first answer option. (String) answer1 The second answer option. (String) answer2 The third answer option. (String) answer3 The fourth answer option. (String) label The correct answer to the question. (String) File: test.csv

Column name Description context The context of the question. (String) answer0 The first answer option. (String) answer1 The second answer option. (String) answer2 The third answer option. (String) answer3 The fourth answer option. (String) label The correct answer to the question. (String)

License

CC0

Original Data Source: Cosmos QA (Commonsense QA)