title: Modeling Complex Mathematical Reasoning via Large Language Model based
    MathAgent
summary: 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.
paper_authors._id: 657bb2c5e37d702c1d6554dd
paper_authors.name: Haoran Liao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T11:04:03.393Z
paper_authors._id: 6337affee30ff02d208d83d3
paper_authors.avatarUrl: /avatars/400f650b3146e946bc83c4dde03de434.svg
paper_authors.fullname: Haoran Liao
paper_authors.type: user
paper_authors.user: HRLiao
paper_authors._id: 657bb2c5e37d702c1d6554de
paper_authors.name: Qinyi Du
paper_authors._id: 657bb2c5e37d702c1d6554df
paper_authors.name: Shaohua Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T11:05:15.788Z
paper_authors._id: 65279be1cb4179303d987e14
paper_authors.avatarUrl: /avatars/1c6a107559642399441917ebad9c6235.svg
paper_authors.fullname: hushaohua
paper_authors.type: user
paper_authors.user: hushaohua2024
paper_authors._id: 657bb2c5e37d702c1d6554e0
paper_authors.name: Hao He
paper_authors._id: 657bb2c5e37d702c1d6554e1
paper_authors.name: Yanyan Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T11:05:41.968Z
paper_authors._id: 64941793603fc88cb421e4e8
paper_authors.avatarUrl: /avatars/300090190e58f52b7441e00917186326.svg
paper_authors.fullname: Yanyan Xu
paper_authors.type: user
paper_authors.user: yxu138
paper_authors._id: 657bb2c5e37d702c1d6554e2
paper_authors.name: Jidong Tian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T11:05:48.643Z
paper_authors._id: 63fd8b17d9818304e858cd03
paper_authors.avatarUrl: /avatars/382afee67ced8258d60a6763ac7c44a2.svg
paper_authors.fullname: Jidong Tian
paper_authors.type: user
paper_authors.user: tjd92Liuhuo
paper_authors._id: 657bb2c5e37d702c1d6554e3
paper_authors.name: Yaohui Jin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-15T11:05:55.135Z
paper_authors._id: 647caf6b1b6ecd15f4bc8cad
paper_authors.avatarUrl: /avatars/8950c0b2878c8af570ac8d7323fbb40d.svg
paper_authors.fullname: Yaohui Jin
paper_authors.type: user
paper_authors.user: brightjin
paper_title: Modeling Complex Mathematical Reasoning via Large Language Model based
    MathAgent
paper_summary: 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.
paper_ai_keywords: Large language models (LLMs) Mathematical reasoning process Agent-based framework Planner-Reasoner-Executor-Reflector (PRER) MathAgent-M MathAgent-H MiniF2F MATH