Mathematical Information Retrieval: Search and Question Answering
- URL: http://arxiv.org/abs/2408.11646v1
- Date: Wed, 21 Aug 2024 14:17:24 GMT
- Title: Mathematical Information Retrieval: Search and Question Answering
- Authors: Richard Zanibbi, Behrooz Mansouri, Anurag Agarwal,
- Abstract summary: multimodal search engines and mathematical question answering systems help answer math-related questions.
This book begins with a simple framework characterizing the information tasks that people and systems perform as we work to answer math-related questions.
- Score: 6.192472816262214
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Mathematical information is essential for technical work, but its creation, interpretation, and search are challenging. To help address these challenges, researchers have developed multimodal search engines and mathematical question answering systems. This book begins with a simple framework characterizing the information tasks that people and systems perform as we work to answer math-related questions. The framework is used to organize and relate the other core topics of the book, including interactions between people and systems, representing math formulas in sources, and evaluation. We close with some key questions and concrete directions for future work. This book is intended for use by students, instructors, and researchers, and those who simply wish that it was easier to find and use mathematical information
Related papers
- A Survey of Deep Learning for Geometry Problem Solving [72.22844763179786]
This paper provides a survey of the applications of deep learning in geometry problem solving.<n>It includes (i) a comprehensive summary of the relevant tasks in geometry problem solving; (ii) a thorough review of related deep learning methods; and (iii) a detailed analysis of evaluation metrics and methods.<n>Our goal is to provide a comprehensive and practical reference of deep learning for geometry problem solving to promote further developments in this field.
arXiv Detail & Related papers (2025-07-16T06:03:08Z) - MIRB: Mathematical Information Retrieval Benchmark [4.587376749548757]
We introduce MIRB (Mathematical Information Retrieval Benchmark) to assess the MIR capabilities of retrieval models.<n>MIRB includes four tasks: semantic statement retrieval, question-answer retrieval, premise retrieval, and formula retrieval, spanning a total of 12 datasets.<n>We evaluate 13 retrieval models on this benchmark and analyze the challenges inherent to MIR.
arXiv Detail & Related papers (2025-05-21T14:40:27Z) - MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs [13.756898876556455]
We propose a novel system, MathMistake Checker, to automate step-by-step mistake finding in mathematical problems with lengthy answers.
The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective.
arXiv Detail & Related papers (2025-03-06T10:19:01Z) - FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI [2.0608396919601493]
FrontierMath is a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians.
Current state-of-the-art AI models solve under 2% of problems, revealing a vast gap between AI capabilities and the prowess of the mathematical community.
As AI systems advance toward expert-level mathematical abilities, FrontierMath offers a rigorous testbed that quantifies their progress.
arXiv Detail & Related papers (2024-11-07T17:07:35Z) - Towards a Holistic Understanding of Mathematical Questions with
Contrastive Pre-training [65.10741459705739]
We propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo.
We first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes.
Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy.
arXiv Detail & Related papers (2023-01-18T14:23:29Z) - A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade.
Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
arXiv Detail & Related papers (2022-12-20T18:46:16Z) - Automatic Generation of Socratic Subquestions for Teaching Math Word
Problems [16.97827669744673]
We explore the ability of large language models (LMs) in generating sequential questions for guiding math word problem-solving.
On both automatic and human quality evaluations, we find that LMs constrained with desirable question properties generate superior questions.
Results suggest that the difficulty level of problems plays an important role in determining whether questioning improves or hinders human performance.
arXiv Detail & Related papers (2022-11-23T10:40:22Z) - JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem
Understanding [74.12405417718054]
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model(PLM)
Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement.
We design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
arXiv Detail & Related papers (2022-06-13T17:03:52Z) - A Neural Network Solves and Generates Mathematics Problems by Program
Synthesis: Calculus, Differential Equations, Linear Algebra, and More [8.437319139670116]
We turn questions into programming tasks, automatically generate programs, and then execute them.
This is the first work to automatically solve, grade, and generate university-level Mathematics course questions at scale.
arXiv Detail & Related papers (2021-12-31T18:57:31Z) - Learning to Match Mathematical Statements with Proofs [37.38969121408295]
The task is designed to improve the processing of research-level mathematical texts.
We release a dataset for the task, consisting of over 180k statement-proof pairs.
We show that considering the assignment problem globally and using weighted bipartite matching algorithms helps a lot in tackling the task.
arXiv Detail & Related papers (2021-02-03T15:38:54Z) - Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document.
We show that readers engage in a series of pragmatic strategies to seek information.
We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
arXiv Detail & Related papers (2020-10-04T19:03:39Z) - Machine Number Sense: A Dataset of Visual Arithmetic Problems for
Abstract and Relational Reasoning [95.18337034090648]
We propose a dataset, Machine Number Sense (MNS), consisting of visual arithmetic problems automatically generated using a grammar model--And-Or Graph (AOG)
These visual arithmetic problems are in the form of geometric figures.
We benchmark the MNS dataset using four predominant neural network models as baselines in this visual reasoning task.
arXiv Detail & Related papers (2020-04-25T17:14:58Z) - A Survey on Knowledge Graphs: Representation, Acquisition and
Applications [89.78089494738002]
We review research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications.
For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed.
We explore several emerging topics, including meta learning, commonsense reasoning, and temporal knowledge graphs.
arXiv Detail & Related papers (2020-02-02T13:17:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.