Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics

• URL: http://arxiv.org/abs/2307.02502v1
• Date: Tue, 4 Jul 2023 20:16:32 GMT
• Title: Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics
• Authors: Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos
• Abstract summary: Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics" Project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics", using a GPT-based workflow to convert equations from literature into LaTeX and Python formats. While many digital equation representations exist, there's a lack of automated large-scale evaluation tools. LLMs are pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. Given the infinite formal possibility spaces, Math Agents, which interact with math, could potentially shift us from "big data" to "big math". Math, unlike the more flexible natural language, has properties subject to proof, enabling its use beyond traditional applications like high-validation math-certified icons for AI alignment aims. This project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology by applying multiscalar physics mathematics to disease models and genomic data. Generative AI with episodic memory could help analyse causal relations in longitudinal health records, using SIR Precision Health models. Genomic data is suggested for addressing the unsolved Alzheimer's disease problem.

Related papers

• 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)
• MathLearner: A Large Language Model Agent Framework for Learning to Solve Mathematical Problems [0.936726079405677]
  We propose an agent framework for learning to solve mathematical problems based on inductive reasoning. By emulating the human learning process of generalization of learned information, this framework has great performance in the mathematical reasoning process. Our model can be used as a personalised learning aid, thus reducing the inequality of educational resources.
  arXiv  Detail & Related papers  (2024-08-03T13:28:19Z)
• Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks [34.09857430966818]
  We introduce an extensive mathematics dataset called "MathQuest" sourced from the 11th and 12th standard Mathematics NCERT textbooks. We conduct fine-tuning experiments with three prominent large language models: LLaMA-2, WizardMath, and MAmmoTH. Our experiments reveal that among the three models, MAmmoTH-13B emerges as the most proficient, achieving the highest level of competence in solving the presented mathematical problems.
  arXiv  Detail & Related papers  (2024-04-19T08:45:42Z)
• MathScale: Scaling Instruction Tuning for Mathematical Reasoning [70.89605383298331]
  Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data.
  arXiv  Detail & Related papers  (2024-03-05T11:42:59Z)
• MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning [2.9104279358536647]
  We present MathSensei, a tool-augmented large language model for mathematical reasoning. We study the complementary benefits of the tools - knowledge retriever (Bing Web Search), program generator + executor (Python), and symbolic equation solver (Wolfram-Alpha API)
  arXiv  Detail & Related papers  (2024-02-27T05:50:35Z)
• Large Language Models for Mathematicians [53.27302720305432]
  Large language models (LLMs) have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code. In this note, we discuss to what extent they can aid professional mathematicians.
  arXiv  Detail & Related papers  (2023-12-07T18:59:29Z)
• math-PVS: A Large Language Model Framework to Map Scientific Publications to PVS Theories [10.416375584563728]
  This work investigates the applicability of large language models (LLMs) in formalizing advanced mathematical concepts. We envision an automated process, called emphmath-PVS, to extract and formalize mathematical theorems from research papers.
  arXiv  Detail & Related papers  (2023-10-25T23:54:04Z)
• MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning [52.97768001837269]
  We present a method to fine-tune open-source language models, enabling them to use code for modeling and deriving math equations. We propose a method of generating novel and high-quality datasets with math problems and their code-based solutions. This approach yields the MathCoder models, a family of models capable of generating code-based solutions for solving challenging math problems.
  arXiv  Detail & Related papers  (2023-10-05T17:52:09Z)
• Evaluating Language Models for Mathematics through Interactions [116.67206980096513]
  We introduce CheckMate, a prototype platform for humans to interact with and evaluate large language models (LLMs) We conduct a study with CheckMate to evaluate three language models (InstructGPT, ChatGPT, and GPT-4) as assistants in proving undergraduate-level mathematics. We derive a taxonomy of human behaviours and uncover that despite a generally positive correlation, there are notable instances of divergence between correctness and perceived helpfulness.
  arXiv  Detail & Related papers  (2023-06-02T17:12:25Z)
• 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)
• Measuring Mathematical Problem Solving With the MATH Dataset [55.4376028963537]
  We introduce MATH, a dataset of 12,500 challenging competition mathematics problems. Each problem has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. We also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.
  arXiv  Detail & Related papers  (2021-03-05T18:59:39Z)

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.