Abstract

Machine Common Sense Reasoning is the subfield of Artificial Intelligence that aims to enable machines to behave or make decisions similarly to humans in everyday and ordinary situations. To measure progress, benchmarks in the form of question-answering datasets have been developed and published in the community to evaluate machine commonsense models, including large language models. We describe the individual label data produced by six human annotators originally used in computing ground truth for the Theoretically-Grounded Commonsense Reasoning (TG-CSR) benchmark's composing datasets. According to a set of instructions, annotators were provided with spreadsheets containing the original TG-CSR prompts and asked to insert labels in specific spreadsheet cells during annotation sessions. TG-CSR data is organized in JSON files, individual raw label data in a spreadsheet file, and …

Date

December 1, 2023

Authors

Henrique Santos, Alice M Mulvehill, Ke Shen, Mayank Kejriwal, Deborah L McGuinness

Journal

Data in Brief

Volume

51

Pages

109666

Publisher

Elsevier