Mathematical Reasoning
114 papers with code • 5 benchmarks • 15 datasets
Benchmarks
These leaderboards are used to track progress in Mathematical Reasoning
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Libraries
Use these libraries to find Mathematical Reasoning models and implementations
MMLU
GSM8K
SVAMP
MathVista
IconQA
PrOntoQA
Lila
TheoremQA
PGPS9K
CLEVR-Math
Subtasks
Most implemented papers
Analysing Mathematical Reasoning Abilities of Neural Models
The structured nature of the mathematics domain, covering arithmetic, algebra, probability and calculus, enables the construction of training and test splits designed to clearly illuminate the capabilities and failure-modes of different architectures, as well as evaluate their ability to compose and relate knowledge and learned processes.
Mistral 7B
We introduce Mistral 7B v0. 1, a 7-billion-parameter language model engineered for superior performance and efficiency.
Measuring Mathematical Problem Solving With the MATH Dataset
hendrycks/math
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To facilitate future research and increase accuracy on MATH, we also contribute a large auxiliary pretraining dataset which helps teach models the fundamentals of mathematics.
FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
With the above challenges in mind, in this paper, we propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models (e. g., ChatGPT).
Compositional Generalization with Tree Stack Memory Units
We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain.
Training Verifiers to Solve Math Word Problems
State-of-the-art language models can match human performance on many tasks, but they still struggle to robustly perform multi-step mathematical reasoning.
IsarStep: a Benchmark for High-level Mathematical Reasoning
In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget.
Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning
However, it is unknown if the models can handle more complex problems that involve math reasoning over heterogeneous information, such as tabular data.
