title: SQuARE: Sequential Question Answering Reasoning Engine for Enhanced
    Chain-of-Thought in Large Language Models
summary: In the rapidly evolving field of Natural Language Processing, Large Language
  Models (LLMs) are tasked with increasingly complex reasoning challenges.
  Traditional methods like chain-of-thought prompting have shown promise but
  often fall short in fully leveraging a model's reasoning capabilities. This
  paper introduces SQuARE (Sequential Question Answering Reasoning Engine), a
  novel prompting technique designed to improve reasoning through a
  self-interrogation paradigm. Building upon CoT frameworks, SQuARE prompts
  models to generate and resolve multiple auxiliary questions before tackling the
  main query, promoting a more thorough exploration of various aspects of a
  topic. Our expansive evaluations, conducted with Llama 3 and GPT-4o models
  across multiple question-answering datasets, demonstrate that SQuARE
  significantly surpasses traditional CoT prompts and existing
  rephrase-and-respond methods. By systematically decomposing queries, SQuARE
  advances LLM capabilities in reasoning tasks. The code is publicly available at
  https://github.com/IntelLabs/RAG-FiT/tree/square.
paper_authors._id: 67aef17da9f929ce0ca3e36b
paper_authors.name: Daniel Fleischer
paper_authors.status: extracted_pending
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paper_authors.fullname: Daniel Fleischer
paper_authors.type: user
paper_authors.user: danf
paper_authors._id: 67aef17da9f929ce0ca3e36c
paper_authors.name: Moshe Berchansky
paper_authors.status: admin_assigned
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paper_authors.fullname: Moshe Berchansky
paper_authors.type: user
paper_authors.user: mber
paper_authors._id: 67aef17da9f929ce0ca3e36d
paper_authors.name: Gad Markovits
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paper_authors.fullname: Gad Markovits
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paper_authors.user: gadmarkovits
paper_authors._id: 67aef17da9f929ce0ca3e36e
paper_authors.name: Moshe Wasserblat
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-14T12:52:19.452Z
paper_authors._id: 6013c64031d84a3bb7650038
paper_authors.avatarUrl: /avatars/e05ca715004b39e79472399f75010bda.svg
paper_authors.fullname: Moshe Wasserblat
paper_authors.type: user
paper_authors.user: moshew
paper_title: SQuARE: Sequential Question Answering Reasoning Engine for Enhanced
    Chain-of-Thought in Large Language Models
paper_summary: In the rapidly evolving field of Natural Language Processing, Large Language
  Models (LLMs) are tasked with increasingly complex reasoning challenges.
  Traditional methods like chain-of-thought prompting have shown promise but
  often fall short in fully leveraging a model's reasoning capabilities. This
  paper introduces SQuARE (Sequential Question Answering Reasoning Engine), a
  novel prompting technique designed to improve reasoning through a
  self-interrogation paradigm. Building upon CoT frameworks, SQuARE prompts
  models to generate and resolve multiple auxiliary questions before tackling the
  main query, promoting a more thorough exploration of various aspects of a
  topic. Our expansive evaluations, conducted with Llama 3 and GPT-4o models
  across multiple question-answering datasets, demonstrate that SQuARE
  significantly surpasses traditional CoT prompts and existing
  rephrase-and-respond methods. By systematically decomposing queries, SQuARE
  advances LLM capabilities in reasoning tasks. The code is publicly available at
  https://github.com/IntelLabs/RAG-FiT/tree/square.
paper_ai_keywords: Large Language Models (LLMs) chain-of-thought prompting Sequential Question Answering Reasoning Engine (SQuARE) self-interrogation paradigm CoT frameworks main query auxiliary Questions question-answering datasets traditional CoT prompts rephrase-and-respond methods reasoning tasks