mlsanityData to 14 Oct 2026

Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering

arXiv 2304.13911 ↗First posted 27 Apr 2023cs.AIcited by 14

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

We investigate how to enhance answer precision in frequently asked questions posed by distributed users using cloud-based Large Language Models (LLMs). Our study focuses on a typical situations where users ask similar queries that involve identical mathematical reasoning steps and problem-solving procedures. Due to the unsatisfactory accuracy of LLMs' zero-shot prompting with standalone questions, we propose to improve the distributed synonymous questions using Self-Consistency (SC) and Chain-of-Thought (CoT) techniques. Specifically, we first retrieve synonymous questions from a crowd-sourced database and create a federated question pool. We call these federated synonymous questions with the same or different parameters SP-questions or DP-questions, respectively. We refer to our methods as Fed-SP-SC and Fed-DP-CoT, which can generate significantly more accurate answers for all user queries without requiring sophisticated model-tuning. Through extensive experiments, we demonstrate that our proposed methods can significantly enhance question accuracy by fully exploring the synonymous nature of the questions and the consistency of the answers.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 23 of its 32 references that are in the map. First posted on arXiv 27 Apr 2023.

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23 of 32 references.

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  1. 2022Large Language Models Can Self-Improve20 Oct

Other references · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2022Training language models to follow instructions with human feedbackcited by 15,971
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  7. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  8. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  9. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  10. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  11. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  12. 2023A Survey of Large Language Modelscited by 2,576
10 more
  1. 2018Deep contextualized word representationscited by 2,290
  2. 2019Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelismcited by 1,868
  3. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  4. 2018Federated Learning with Non-IID Datacited by 1,108
  5. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 878
  6. 2021Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attentioncited by 40
  7. 2022Standing on the Shoulders of Giant Frozen Language Modelscited by 33
  8. 2021Conversational Question Answering: A Surveycited by 31
  9. 2022Automatic Text Summarization Methods: A Comprehensive Reviewcited by 17
  10. 2021Federated Few-Shot Learning with Adversarial Learningcited by 11

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Citing papers per year: 2023: 4; 2024: 6; 2025: 3; 2026: 1.

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  1. 2023HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Modelscited by 63
  2. 2024Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 49
  3. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 47
  4. 2023Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learningcited by 29
  5. 2024Federated Large Language Models: Current Progress and Future Directionscited by 27
  6. 2025Optuna vs Code Llama: Are LLMs a New Paradigm for Hyperparameter Tuning?cited by 21
  7. 2025Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluationcited by 21
  8. 2023Sci-CoT: Leveraging Large Language Models for Enhanced Knowledge Distillation in Small Models for Scientific QAcited by 13
  9. 2023Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospectscited by 6
  10. 2024Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Modelscited by 2
  11. 2024Heterogeneous Federated Learning with Splited Language Modelcited by 1
  12. 2026FERA: Uncertainty-Aware Federated Reasoning for Large Language Modelscited by 1
2 more
  1. 2024An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problemscited by 0
  2. 2025Systematic Optimization of Open Source Large Language Models for Mathematical Reasoningcited by 0

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  2. 2023Universal Self-Consistency for Large Language Model Generation
  3. 2025SQuARE: Sequential Question Answering Reasoning Engine for Enhanced Chain-of-Thought in Large Language Models
  4. 2024Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering
  5. 2025Empowering LLMs with Logical Reasoning: A Comprehensive Survey
  6. 2024Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch
  7. 2023Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs
  8. 2024Reasoning Aware Self-Consistency: Leveraging Reasoning Paths for Efficient LLM Sampling