GSM8K - Grade School Math 8K Q&A

A Linguistically Diverse Dataset for Multi-Step Reasoning Question Answering

By Huggingface Hub [source]

About this dataset

This Grade School Math 8K Linguistically Diverse Training & Test Set is designed to help you develop and improve your understanding of multi-step reasoning question answering. The dataset contains three separate data files: the socratic_test.csv, main_test.csv, and main_train.csv, each containing a set of questions and answers related to grade school math that consists of multiple steps. Each file contains the same columns: question, answer. The questions contained in this dataset are thoughtfully crafted to lead you through the reasoning journey for arriving at the correct answer each time, allowing you immense opportunities for learning through practice. With over 8 thousand entries for both training and testing purposes in this GSM8K dataset, it takes advanced multi-step reasoning skills to ace these questions! Deepen your knowledge today and master any challenge with ease using this amazing GSM8K set!

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How to use the dataset

This dataset provides a unique opportunity to study multi-step reasoning for question answering. The GSM8K Linguistically Diverse Training & Test Set consists of 8,000 questions and answers that have been created to simulate real-world scenarios in grade school mathematics. Each question is paired with one answer based on a comprehensive test set. The questions cover topics such as algebra, arithmetic, probability and more.

The dataset consists of two files: main_train.csv and main_test.csv; the former contains questions and answers specifically related to grade school math while the latter includes multi-step reasoning tests for each category of the Ontario Math Curriculum (OMC). In addition, it has three columns - Question (Question), Answer ([Answer]) – meaning that each row contains 3 sequential question/answer pairs making it possible to take a single path from the start of any given answer or branch out from there according to the logic construction required by each respective problem scenario; these columns can be used in combination with text analysis algorithms like ELMo or BERT to explore different formats of representation for responding accurately during natural language processing tasks such as Q&A or building predictive models for numerical data applications like measuring classifying resource efficiency initiatives or forecasting sales volumes in retail platforms..

To use this dataset efficiently you should first get familiar with its structure by reading through its documentation so you are aware all available info regarding items content definition & format requirements then study examples that best suits your specific purpose whether is performing an experiment inspired by education research needs, generate insights related marketing analytics reports making predictions over artificial intelligence project capacity improvements optimization gains etcetera having full access knowledge about available source keeps you up & running from preliminary background work toward knowledge mining endeavor completion success Support User success qualitative exploration sessions make sure learn all variables definitions employed heterogeneous tools before continue Research journey starts experienced Researchers come prepared valuable resource items employed go beyond discovery false alarm halt advancement flow focus unprocessed raw values instead ensure clear cutting vision behind objectives support UserHelp plans going mean project meaningful campaign deliverables production planning safety milestones dovetail short deliveries enable design interfaces session workforce making everything automated fun entry functioning final transformation awaited offshoot Goals outcome parameters monitor life cycle management ensures ongoing projects feedbacks monitored video enactment resources tapped Proficiently balanced activity sheets tracking activities progress deliberation points evaluation radius highlights outputs primary phase visit egress collaboration agendas Client cumulative returns records capture performance illustrated collectively diarized successive setup sweetens conditions researched environments overview debriefing arcane matters turn acquaintances esteemed directives social

Research Ideas

• Training language models for improving accuracy in natural language processing applications such as question answering or dialogue systems.
• Generating new grade school math questions and answers using g...

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Data from a comparative judgement survey consisting of 62 working mathematics educators (ME) at Norwegian universities or city colleges, and 57 working mathematicians at Norwegian universities. A total of 3607 comparisons of which 1780 comparisons by the ME and 1827 ME. The comparative judgement survey consisted of respondents comparing pairs of statements on mathematical definitions compiled from a literature review on mathematical definitions in the mathematics education literature. Each WM was asked to judge 40 pairs of statements with the following question: “As a researcher in mathematics, where your target group is other mathematicians, what is more important about mathematical definitions?” Each ME was asked to judge 41 pairs of statements with the following question: “For a mathematical definition in the context of teaching and learning, what is more important?” The comparative judgement was done with No More Marking software (nomoremarking.com) The data set consists of the following data: comparisons made by ME (ME.csv) comparisons made by WM (WM.csv) Look up table of codes of statements and statement formulations (key.csv) Each line in the comparison represents a comparison, where the "winner" column represents the winner and the "loser" column the loser of the comparison.

Abstract: Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited. Supply chains are inherently graph-like, making them ideal for GNN methodologies, which can optimize and solve complex problems. The barriers include a lack of proper conceptual foundations, familiarity with graph applications in SCM, and real-world benchmark datasets for GNN-based supply chain research. To address this, we discuss and connect supply chains with graph structures for effective GNN application, providing detailed formulations, examples, mathematical definitions, and task guidelines. Additionally, we present a multi-perspective real-world benchmark dataset from a leading FMCG company in Bangladesh, focusing on supply chain planning. We discuss various supply chain tasks using GNNs and benchmark several state-of-the-art models on homogeneous and heterogeneous graphs across six supply chain analytics tasks. Our analysis shows that GNN-based models consistently outperform statistical ML and other deep learning models by around 10-30% in regression, 10-30% in classification and detection tasks, and 15-40% in anomaly detection tasks on designated metrics. With this work, we lay the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.

UniMath Q&A Dataset

  Dataset Description

The UniMath Q&A Dataset is a conversational extension of the UniMath Dataset, derived from the UniMath formalization of mathematics (https://github.com/UniMath/UniMath). This dataset transforms Univalent Mathematics content into structured Q&A pairs, making formal mathematical content more accessible through natural language interactions. Each entry represents a mathematical statement from UniMath (definition, theorem, lemma, etc.)… See the full description on the dataset page: https://huggingface.co/datasets/phanerozoic/Coq-UniMath-QA.

Definitions of mathematical notation used in this paper.

The release of the LCA Commons Unit Process Data: field crop production Version 1.1 includes the following updates:Added meta data to reflect USDA LCA Digital Commons data submission guidance including descriptions of the process (reference to which the size of the inputs and outputs in the process relate, description of the process and technical scope and any aggregation; definition of the technology being used, its operating conditions); temporal representatives; geographic representativeness; allocation methods; process type (U: unit process, S: system process); treatment of missing intermediate flow data; treatment of missing flow data to or from the environment; intermediate flow data sources; mass balance; data treatment (description of the methods and assumptions used to transform primary and secondary data into flow quantities through recalculating, reformatting, aggregation, or proxy data and a description of data quality according to LCADC convention); sampling procedures; and review details. Also, dataset documentation and related archival publications are cited in the APA format.Changed intermediate flow categories and subcategories to reflect the ISIC International Standard Industrial Classification (ISIC).Added “US-” to the US state abbreviations for intermediate flow locations.Corrected the ISIC code for “CUTOFF domestic barge transport; average fuel” (changed to ISIC 5022: Inland freight water transport).Corrected flow names as follows: "Propachlor" renamed "Atrazine". “Bromoxynil octanoate” renamed “Bromoxynil heptanoate”. “water; plant uptake; biogenic” renamed “water; from plant uptake; biogenic” half the instances of “Benzene, pentachloronitro-“ replaced with Etridiazole and half with “Quintozene”. “CUTOFF phosphatic fertilizer, superphos. grades 22% & under; at point-of-sale” replaced with “CUTOFF phosphatic fertilizer, superphos. grades 22% and under; at point-of-sale”.Corrected flow values for “water; from plant uptake; biogenic” and “dry matter except CNPK; from plant uptake; biogenic” in some datasets.Presented data in the International Reference Life Cycle Data System (ILCD)1 format, allowing the parameterization of raw data and mathematical relations to be presented within the datasets and the inclusion of parameter uncertainty data. Note that ILCD formatted data can be converted to the ecospold v1 format using the OpenLCA software.Data quality rankings have been updated to reflect the inclusion of uncertainty data in the ILCD formatted data.Changed all parameter names to “pxxxx” to accommodate mathematical relation character limitations in OpenLCA. Also adjusted select mathematical relations to recognize zero entries. The revised list of parameter names is provided in the documentation attached.Resources in this dataset:Resource Title: Cooper-crop-production-data-parameterization-version-1.1 .File Name: Cooper-crop-production-data-parameterization-version-1.1.xlsxResource Description: Description of parameters that define the Cooper Unit process data for field crop production version 1.1Resource Title: Cooper_Crop_Data_v1.1_ILCD.File Name: Cooper_Crop_Data_v1.1_ILCD.zipResource Description: .zip archive of ILCD xml files that comprise crop production unit process modelsResource Software Recommended: openLCA,url: http://www.openlca.org/Resource Title: Summary of Revisions of the LCA Digital Commons Unit Process Data: field crop production for version 1.1 (August 2013).File Name: Summary of Revisions of the LCA Digital Commons Unit Process Data- field crop production, Version 1.1 (August 2013).pdfResource Description: Documentation of revisions to version 1 data that constitute version 1.1

CONTEXT

Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment,​ the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.

The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.

The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.

DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).

Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html

Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE

COLUMN NOTES

All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.

FEDERAL FINANCE DATA DEFINITIONS

t_fed_rev: Total federal revenue through the state to each school district.

C14- Federal revenue through the state- Title 1 (no child left behind act).

C25- Federal revenue through the state- Child Nutrition Act.

Title 1 is a program implemented in schools to help raise academic achievement ​for all students. The program is available to schools where at least 40% of the students come from low inccom​​e families.

Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income​ families.

MATH SCORES DATA DEFINITIONS

Note: Mathematics, Grade 8, 2017, All Students (Total)

average_scale_score - The state's average score for eighth graders taking the NAEP math exam.

READ ME

Welcome to the Universal Binary Principle (UBP) Dictionary System - Version 2

Author: Euan Craig, New Zealand 2025

Embark on a revolutionary journey with Version 2 of the UBP Dictionary System, a cutting-edge Python notebook that redefines how words are stored, analyzed, and visualized! Built for Kaggle, this system encodes words as multidimensional hexagonal structures in custom .hexubp files, leveraging sophisticated mathematics to integrate binary toggles, resonance frequencies, spatial coordinates, and more, all rooted in the Universal Binary Principle (UBP). This is not just a dictionary—it’s a paradigm shift in linguistic representation.

What is the UBP Dictionary System? The UBP Dictionary System transforms words into rich, vectorized representations stored in custom .hexubp files—a JSON-based format designed to encapsulate a word’s multidimensional UBP properties. Each .hexubp file represents a word as a hexagonal structure with 12 vertices, encoding: * Binary Toggles: 6-bit patterns capturing word characteristics. * Resonance Frequencies: Derived from the Schumann resonance (7.83 Hz) and UBP Pi (~2.427). * Spatial Vectors: 6D coordinates positioning words in a conceptual “Bitfield.” * Cultural and Harmonic Data: Contextual weights, waveforms, and harmonic properties.

These .hexubp files are generated, managed, and visualized through an interactive Tkinter-based interface, making the system a powerful tool for exploring language through a mathematical lens.

Unique Mathematical Foundation The UBP Dictionary System is distinguished by its deep reliance on mathematics to model language: * UBP Pi (~2.427): A custom constant derived from hexagonal geometry and resonance alignment (calculated as 6/2 * cos(2π * 7.83 * 0.318309886)), serving as the system’s foundational reference. * Resonance Frequencies: Frequencies are computed using word-specific hashes modulated by UBP Pi, with validation against the Schumann resonance (7.83 Hz ± 0.078 Hz), grounding the system in physical phenomena. * 6D Spatial Vectors: Words are positioned in a 6D Bitfield (x, y, z, time, phase, quantum state) based on toggle sums and frequency offsets, enabling spatial analysis of linguistic relationships. * GLR Validation: A non-corrective validation mechanism flags outliers in binary, frequency, and spatial data, ensuring mathematical integrity without compromising creativity.

This mathematical rigor sets the system apart from traditional dictionaries, offering a framework where words are not just strings but dynamic entities with quantifiable properties. It’s a fusion of linguistics, physics, and computational theory, inviting users to rethink language as a multidimensional phenomenon.

Comparison with Other Data Storage Mechanisms The .hexubp format is uniquely tailored for UBP’s multidimensional model. Here’s how it compares to other storage mechanisms, with metrics to highlight its strengths: CSV/JSON (Traditional Dictionaries): * Structure: Flat key-value pairs (e.g., word:definition). * Storage: ~100 bytes per word for simple text (e.g., “and”:“conjunction”). * Query Speed: O(1) for lookups, but no support for vector operations. * Limitations: Lacks multidimensional data (e.g., spatial vectors, frequencies). * .hexubp Advantage: Stores 12 vertices with vectors (~1-2 KB per word), enabling complex analyses like spatial clustering or frequency drift detection.

Relational Databases (SQL): * Structure: Tabular, with columns for word, definition, etc. * Storage: ~200-500 bytes per word, plus index overhead. * Query Speed: O(log n) for indexed queries, slower for vector computations. * Limitations: Rigid schema, inefficient for 6D vectors or dynamic vertices. * .hexubp Advantage: Lightweight, file-based (~1-2 KB per word), with JSON flexibility for UBP’s hexagonal model, no database server required.

Vector Databases (e.g., Word2Vec): * Structure: Fixed-dimension vectors (e.g., 300D for semantic embeddings). * Storage: ~2.4 KB per word (300 floats at 8 bytes each). * Query Speed: O(n) for similarity searches, optimized with indexing. * Limitations: Generic embeddings lack UBP-specific dimensions (e.g., resonance, toggles). * .hexubp Advantage: Smaller footprint (~1-2 KB), with domain-specific dimensions tailored to UBP’s theoretical framework.

Graph Databases: * Structure: Nodes and edges for word relationships. * Storage: ~500 bytes per word, plus edge overhead. * Query Speed: O(k) for traversals, where k is edge count. * Limitations: Overkill for dictionary tasks, complex setup. * .hexubp Advantage: Self-contained hexagonal structure per word, simpler for UBP’s needs, with comparable storage (~1-2 KB).

The .hexubp format balances storage efficiency, flexibility, and UBP-s...