Modeling Complex Mathematical Reasoning via Large Language Model based
  MathAgent
Large language models (LLMs) face challenges in solving complex mathematical
problems that require comprehensive capacities to parse the statements,
associate domain knowledge, perform compound logical reasoning, and integrate
the intermediate rationales. Tackling all these problems once could be arduous
for LLMs, thus leading to confusion in generation. In this work, we explore the
potential of enhancing LLMs with agents by meticulous decomposition and
modeling of mathematical reasoning process. Specifically, we propose a formal
description of the mathematical solving and extend LLMs with an agent-based
zero-shot framework named
Planner-Reasoner-Executor-Reflector (PRER). We
further provide and implement two MathAgents that define the logical forms and
inherent relations via a pool of actions in different grains and orientations:
MathAgent-M adapts its actions to LLMs, while MathAgent-H aligns with
humankind. Experiments on miniF2F and MATH have demonstrated the effectiveness
of PRER and proposed MathAgents, achieving an increase of
12.3%(53.9%66.2%) on the MiniF2F, 9.2%
(49.8%59.0%) on MATH, and
13.2%(23.2%35.4%) for level-5 problems of MATH against
GPT-4. Further analytical results provide more insightful perspectives on
exploiting the behaviors of LLMs as agents.
657bb2c5e37d702c1d6554dd
Haoran Liao
admin_assigned
2023-12-15T11:04:03.393Z
6337affee30ff02d208d83d3
/avatars/400f650b3146e946bc83c4dde03de434.svg
Haoran Liao
user
HRLiao
657bb2c5e37d702c1d6554de
Qinyi Du
657bb2c5e37d702c1d6554df
Shaohua Hu
admin_assigned
2023-12-15T11:05:15.788Z
65279be1cb4179303d987e14
/avatars/1c6a107559642399441917ebad9c6235.svg
hushaohua
user
hushaohua2024
657bb2c5e37d702c1d6554e0
Hao He
657bb2c5e37d702c1d6554e1
Yanyan Xu
admin_assigned
2023-12-15T11:05:41.968Z
64941793603fc88cb421e4e8
/avatars/300090190e58f52b7441e00917186326.svg
Yanyan Xu
user
yxu138
657bb2c5e37d702c1d6554e2
Jidong Tian
admin_assigned
2023-12-15T11:05:48.643Z
63fd8b17d9818304e858cd03
/avatars/382afee67ced8258d60a6763ac7c44a2.svg
Jidong Tian
user
tjd92Liuhuo
657bb2c5e37d702c1d6554e3
Yaohui Jin
admin_assigned
2023-12-15T11:05:55.135Z
647caf6b1b6ecd15f4bc8cad
/avatars/8950c0b2878c8af570ac8d7323fbb40d.svg
Yaohui Jin
user
brightjin
Modeling Complex Mathematical Reasoning via Large Language Model based
  MathAgent
Large language models (LLMs) face challenges in solving complex mathematical
problems that require comprehensive capacities to parse the statements,
associate domain knowledge, perform compound logical reasoning, and integrate
the intermediate rationales. Tackling all these problems once could be arduous
for LLMs, thus leading to confusion in generation. In this work, we explore the
potential of enhancing LLMs with agents by meticulous decomposition and
modeling of mathematical reasoning process. Specifically, we propose a formal
description of the mathematical solving and extend LLMs with an agent-based
zero-shot framework named
Planner-Reasoner-Executor-Reflector (PRER). We
further provide and implement two MathAgents that define the logical forms and
inherent relations via a pool of actions in different grains and orientations:
MathAgent-M adapts its actions to LLMs, while MathAgent-H aligns with
humankind. Experiments on miniF2F and MATH have demonstrated the effectiveness
of PRER and proposed MathAgents, achieving an increase of
12.3%(53.9%66.2%) on the MiniF2F, 9.2%
(49.8%59.0%) on MATH, and
13.2%(23.2%35.4%) for level-5 problems of MATH against
GPT-4. Further analytical results provide more insightful perspectives on
exploiting the behaviors of LLMs as agents.
A zero-shot framework PRER with MathAgents enhances LLMs in solving complex mathematical problems by decomposing and modeling the reasoning process.
agents mathematical reasoning PRER MathAgent-M MathAgent-H miniF2F MATH GPT-4 intermediate rationales logical forms domain knowledge compound logical reasoning