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Updated: Jul 12, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
Published on: November 2, 2012
TG-CSR: A human-labeled dataset grounded in nine formal commonsense categories
Henrique Santos1, Alice M Mulvehill1, Ke Shen2
1Rensselaer Polytechnic Institute 110 8th St., Troy, NY 12180, USA.
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 individual normalized label data in JSONL files. The release of individual labels can enable the analysis of the labeling process itself, including studies of noise and consistency across annotators.
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