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
62d93cd728f9c86a4031562e
/avatars/4619930d15512ec9b80b01c62e986217.svg
Daniel Fleischer
user
danf
67aef17da9f929ce0ca3e36c
Moshe Berchansky
admin_assigned
2025-02-14T12:52:07.600Z
63e0c8875c6964861ebb0c49
https://cdn-avatars.huggingface.co/v1/production/uploads/63e0c8875c6964861ebb0c49/yzkhPSxgXtJCM62iMBOOK.jpeg
Moshe Berchansky
user
mber
67aef17da9f929ce0ca3e36d
Gad Markovits
admin_assigned
2025-02-14T12:52:13.549Z
666154a2694ec45eaabc19c3
/avatars/b0022ad6dd992c75a51973070b302315.svg
Gad Markovits
user
gadmarkovits
67aef17da9f929ce0ca3e36e
Moshe Wasserblat
admin_assigned
2025-02-14T12:52:19.452Z
6013c64031d84a3bb7650038
/avatars/e05ca715004b39e79472399f75010bda.svg
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.
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