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This repository contains the publicly released dataset, code, and models for the Explanations for CommonsenseQA paper presented at ACL-IJCNLP 2021. Directories data and code inside the root folder contain dataset and code, respectively. The same data and code are also made available through our AIHN collaboration partner institute IIT Delhi. You can download the full paper from here. Note that these annotations are provided for the questions of the CommonsenseQA data (https://www.tau-nlp.org/commonsenseqa): arXiv:1811.00937 [cs.CL] (or arXiv:1811.00937v2 [cs.CL] for this version). ### Citations Please consider citing this paper as follows: @inproceedings{aggarwaletal2021ecqa, title={{E}xplanations for {C}ommonsense{QA}: {N}ew {D}ataset and {M}odels}, author={Shourya Aggarwal and Divyanshu Mandowara and Vishwajeet Agrawal and Dinesh Khandelwal and Parag Singla and Dinesh Garg}, booktitle="Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)}", Pages = 3050–3065, year = "2021", publisher = "Association for Computational Linguistics" }

Text Retrieval Conference Question Answering (TrecQA) is a dataset created from the TREC-8 (1999) to TREC-13 (2004) Question Answering tracks. There are two versions of TrecQA: raw and clean. Both versions have the same training set but their development and test sets differ. The commonly used clean version of the dataset excludes questions in development and test sets with no answers or only positive/negative answers. The clean version has 1,229/65/68 questions and 53,417/1,117/1,442 question-answer pairs for the train/dev/test split. Source: A Gated Self-attention Memory Network for Answer Selection

SecQA is a specialized dataset created for the evaluation of Large Language Models (LLMs) in the domain of computer security. It consists of multiple-choice questions, generated using GPT-4 and the Computer Systems Security: Planning for Success textbook, aimed at assessing the understanding and application of LLMs' knowledge in computer security.