SQuARE: Sequential Question Answering Reasoning Engine for Enhanced
  Chain-of-Thought in Large Language Models
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.
67aef17da9f929ce0ca3e36b
Daniel Fleischer
extracted_pending
2025-02-14T07:32:14.019Z
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Daniel Fleischer
user
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Moshe Berchansky
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2025-02-14T12:52:07.600Z
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Moshe Berchansky
user
mber
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Gad Markovits
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2025-02-14T12:52:13.549Z
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Gad Markovits
user
gadmarkovits
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Moshe Wasserblat
admin_assigned
2025-02-14T12:52:19.452Z
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Moshe Wasserblat
user
moshew
SQuARE: Sequential Question Answering Reasoning Engine for Enhanced
  Chain-of-Thought in Large Language Models
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.
SQuARE, a novel prompting technique, enhances Large Language Models' reasoning by generating and resolving multiple auxiliary questions before addressing the main query, achieving better results than traditional methods.
Large Language Models (LLMs) chain-of-thought prompting Sequential Question Answering Reasoning Engine (SQuARE) CoT frameworks Llama 3 GPT-4o question-answering datasets rephrase-and-respond methods