LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidates’ critical thinking and problem solving. This dataset includes the English versions only; the Chinese versions are available via the homepage/original source.

fireworks-ai/logiqa dataset hosted on Hugging Face and contributed by the HF Datasets community

LogiQA is a dataset for testing human logical reasoning. It consists of 8,678 QA instances, covering multiple types of deductive reasoning. Results show that state- of-the-art neural models perform by far worse than human ceiling. The dataset can also serve as a benchmark for reinvestigating logical AI under the deep learning NLP setting.

heka-ai/logiqa dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for "agieval-logiqa-zh"

Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo. Raw datset: https://github.com/lgw863/LogiQA-dataset Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) @misc{zhong2023agieval, title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models}, author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu… See the full description on the dataset page: https://huggingface.co/datasets/dmayhem93/agieval-logiqa-zh.

extraordinarylab/logiqa dataset hosted on Hugging Face and contributed by the HF Datasets community

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address ali.khatir@logiqa.co.uk..

xiaoyuanliu/logiqa dataset hosted on Hugging Face and contributed by the HF Datasets community

fireworks-ai/logiqa-deepseek-v3 dataset hosted on Hugging Face and contributed by the HF Datasets community

quzhe/logiqa-en dataset hosted on Hugging Face and contributed by the HF Datasets community

valen02/logiqa-en_AGIEval dataset hosted on Hugging Face and contributed by the HF Datasets community

Fatturato per gli ultimi anni, elenco utili/perdita, costo dipendenti, soci esponenti e contatti per LOGIQA SRL IN LIQUIDAZIONE in Gualtieri (RE)

Logikon Bench

Collection of high quality datasets to evaluate LLM's reasoning abilities. Compared to the original versions, the datasets have been checked for consistency; buggy examples have been removed. In addition, the English logiqa dataset is an entirely new translation of the orginal Chinese dataset. The subdatasets are made available in accordance with the original licenses:

LSAT: MIT License Link: https://github.com/zhongwanjun/AR-LSAT LogiQA: CC BY-NC-SA 4.0 Link:… See the full description on the dataset page: https://huggingface.co/datasets/logikon/logikon-bench.

The dataset is an amendment and re-annotation of LogiQA in 2020, a large-scale logical reasoning reading comprehension dataset adapted from the Chinese Civil Service Examination. We increase the data size, refine the texts with manual translation by professionals, and improve the quality by removing items with distinctive cultural features like Chinese idioms. Furthermore, we conduct a fine-grained annotation on the dataset and turn it into a two-way natural language inference (NLI) task, resulting in 35k premise-hypothesis pairs with gold labels, making it the first large-scale NLI dataset for complex logical reasoning

Rule Reasoning Datasets

This repository contains datasets for rule-based reasoning tasks, organized into two main categories:

  In-Distribution (ID) Datasets

ar_lsat: Analytical Reasoning from LSAT clutrr: CLUTtRR (Compositional Language Understanding and Text-based Relational Reasoning) folio: FOLIO (First-Order Logic in Natural Language) logic_nli: Logic-based Natural Language Inference logical_deduction: Logical Deduction tasks logiqa: LogiQA (Logical Reasoning QA)… See the full description on the dataset page: https://huggingface.co/datasets/RuleReasoner/rule-reasoning.

LogiQA2.0

Logiqa2.0 dataset - logical reasoning in MRC and NLI tasks

This is the official repository for the LogiQA 2.0 datasets in our paper LogiQA2.0 - An Improved Dataset for Logic Reasoning in Question Answering and Textual Inference

  How to cite

@ARTICLE{10174688, author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}… See the full description on the dataset page: https://huggingface.co/datasets/datatune/LogiQA2.0.