MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems
Recent advancements in large language models, such as GPT-4, have demonstrated remarkable capabilities in processing standard queries. Despite these advancements, their performance substantially declines in \textbf{advanced mathematical problems requiring complex, multi-step logical reasoning}. To enhance their inferential capabilities, current research has delved into \textit{prompting engineering}, exemplified by methodologies such as the Tree of Thought and Graph of Thought. Nonetheless, these existing approaches encounter two significant limitations. Firstly, their effectiveness in tackling complex mathematical problems is somewhat constrained. Secondly, the necessity to design distinct prompts for individual problems hampers their generalizability. In response to these limitations, this paper introduces the \textit{Multi-Agent System for conditional Mining} (\textbf{MACM}) prompting method. It not only resolves intricate mathematical problems but also demonstrates strong generalization capabilities across various mathematical contexts. With the assistance of MACM, the accuracy of GPT-4 Turbo on the most challenging level five mathematical problems in the MATH dataset increase from $\mathbf{54.68\%} \text{ to } \mathbf{76.73\%}$. The code is available in \url{https://github.com/bin123apple/MACM}.
Code (1)
Tasks
Logical ReasoningMathMath Word Problem SolvingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MaCmS: Magahi Code-mixed Dataset for Sentiment Analysis
The present paper introduces new sentiment data, MaCMS, for Magahi-Hindi-English (MHE) code-mixed language, where Magahi is a less-resourced minority language. This dataset is the first Magahi-Hindi-English code-mixed da…
Sentiment AnalysisMultiplicative-Additive Constrained Models:Toward Joint Visualization of Interactive and Independent Effects
Interpretability is one of the considerations when applying machine learning to high-stakes fields such as healthcare that involve matters of life safety. Generalized Additive Models (GAMs) enhance interpretability by vi…
MACMD: Multi-dilated Contextual Attention and Channel Mixer Decoding for Medical Image Segmentation
Medical image segmentation faces challenges due to variations in anatomical structures. While convolutional neural networks (CNNs) effectively capture local features, they struggle with modeling long-range dependencies. …
Medical Image SegmentationComputational EfficiencyUsing English Dictionaries to generate Commonsense Knowledge in Natural Language
This paper presents an approach to generating common sense knowledge written in raw English sentences. Instead of using public contributors to feed this source, this system chose to employ expert linguistics decisions by…
Common Sense ReasoningNatural Language InferenceThe role of local bounds on neighborhoods in the network for scale-free state synchronization of multi-agent systems
This paper provides necessary and sufficient conditions for the existence of solutions to the state synchronization problem of homogeneous multi-agent systems (MAS) via scale-free linear dynamic non-collaborative protoco…