mlsanityData to 7 Oct 2026

SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models

arXiv 2502.09390 ↗First posted 13 Feb 2025cs.CL cs.AI cs.LGcited by 1

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 14 of its 20 references that are in the map. First posted on arXiv 13 Feb 2025.

Builds on

14 of 20 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2024The Llama 3 Herd of Modelscited by 14,192
  5. 2024GPT-4o System Cardcited by 4,467
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  7. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameterscited by 1,928
  8. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  9. 2023Universal Self-Consistency for Large Language Model Generationcited by 198
  10. 2023Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselvescited by 123
  11. 2024HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughlycited by 119
  12. 2023A Multi-Task Embedder For Retrieval Augmented LLMscited by 63

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023ThoughtSource: A central hub for large language model reasoning data
  2. 2023Self-prompted Chain-of-Thought on Large Language Models for Open-domain Multi-hop Reasoning
  3. 2024MINTQA: A Multi-Hop Question Answering Benchmark for Evaluating LLMs on New and Tail Knowledge
  4. 2025Early Stopping Chain-of-thoughts in Large Language Models
  5. 2026Exploring the Meta-level Reasoning of Large Language Models via a Tool-based Multi-hop Tabular Question Answering Task
  6. 2024Refining Answer Distributions for Improved Large Language Model Reasoning
  7. 2023Graph Elicitation for Guiding Multi-Step Reasoning in Large Language Models
  8. 2023An automatically discovered chain-of-thought prompt generalizes to novel models and datasets