title: K-Level Reasoning with Large Language Models
summary: While Large Language Models (LLMs) have demonstrated their proficiency in
  complex reasoning tasks, their performance in dynamic, interactive, and
  competitive scenarios - such as business strategy and stock market analysis -
  remains underexplored. To bridge this gap, we formally explore the dynamic
  reasoning capabilities of LLMs for decision-making in rapidly evolving
  environments. We introduce two game theory-based pilot challenges that mirror
  the complexities of real-world dynamic decision-making. These challenges are
  well-defined, enabling clear, controllable, and precise evaluation of LLMs'
  dynamic reasoning abilities. Through extensive experiments, we find that
  existing reasoning methods tend to falter in dynamic settings that require
  k-level thinking - a key concept not tackled by previous works. To address
  this, we propose a novel reasoning approach for LLMs, named "K-Level
  Reasoning". This approach adopts the perspective of rivals to recursively
  employ k-level thinking based on available historical information, which
  significantly improves the prediction accuracy of rivals' subsequent moves and
  informs more strategic decision-making. This research not only sets a robust
  quantitative benchmark for the assessment of dynamic reasoning but also
  markedly enhances the proficiency of LLMs in dynamic contexts.
paper_authors._id: 65c055b4ffe270f2f8ddddd0
paper_authors.name: Yadong Zhang
paper_authors._id: 65c055b4ffe270f2f8ddddd1
paper_authors.name: Shaoguang Mao
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Shaoguang Mao
paper_authors.type: user
paper_authors.user: dawnmsg
paper_authors._id: 65c055b4ffe270f2f8ddddd2
paper_authors.name: Tao Ge
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-02-05T05:45:55.402Z
paper_authors._id: 64ab5db78a1a9187c215f45a
paper_authors.avatarUrl: /avatars/17add4cd02e183f9b9d4feb7b6c27c21.svg
paper_authors.fullname: Tao Ge
paper_authors.type: user
paper_authors.user: sggetao
paper_authors._id: 65c055b4ffe270f2f8ddddd3
paper_authors.name: Xun Wang
paper_authors._id: 65c055b4ffe270f2f8ddddd4
paper_authors.name: Yan Xia
paper_authors._id: 65c055b4ffe270f2f8ddddd5
paper_authors.name: Man Lan
paper_authors._id: 65c055b4ffe270f2f8ddddd6
paper_authors.name: Furu Wei
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-05T12:01:23.100Z
paper_authors._id: 6368c512fbfe97c16a40baba
paper_authors.avatarUrl: /avatars/1c23bc7c0b6d9225699ce27647623d7a.svg
paper_authors.fullname: Furu Wei
paper_authors.type: user
paper_authors.user: thegenerality
paper_title: K-Level Reasoning with Large Language Models
paper_summary: While Large Language Models (LLMs) have demonstrated their proficiency in
  complex reasoning tasks, their performance in dynamic, interactive, and
  competitive scenarios - such as business strategy and stock market analysis -
  remains underexplored. To bridge this gap, we formally explore the dynamic
  reasoning capabilities of LLMs for decision-making in rapidly evolving
  environments. We introduce two game theory-based pilot challenges that mirror
  the complexities of real-world dynamic decision-making. These challenges are
  well-defined, enabling clear, controllable, and precise evaluation of LLMs'
  dynamic reasoning abilities. Through extensive experiments, we find that
  existing reasoning methods tend to falter in dynamic settings that require
  k-level thinking - a key concept not tackled by previous works. To address
  this, we propose a novel reasoning approach for LLMs, named "K-Level
  Reasoning". This approach adopts the perspective of rivals to recursively
  employ k-level thinking based on available historical information, which
  significantly improves the prediction accuracy of rivals' subsequent moves and
  informs more strategic decision-making. This research not only sets a robust
  quantitative benchmark for the assessment of dynamic reasoning but also
  markedly enhances the proficiency of LLMs in dynamic contexts.
paper_ai_summary: Research introduces "K-Level Reasoning" to enhance LLM performance in dynamic, interactive scenarios using game theory-based challenges and recursive k-level thinking.
paper_ai_keywords: Large Language Models LLMs dynamic reasoning interactive scenarios game theory k-level thinking K-Level Reasoning rivals prediction accuracy strategic decision-making