mlsanityData to 7 Oct 2026

Chain of Thought Prompting Elicits Knowledge Augmentation

arXiv 2307.01640 ↗First posted 4 Jul 2023cs.CLcited by 21

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

Abstract (arXiv)

The knowledge-augmented deep learning paradigm refers to a paradigm in which domain knowledge is identified and integrated into deep models. Conventional methods typically employ task-specific approaches to gather external knowledge from various sources. In contrast, large language models are extensively pre-trained and can serve as a comprehensive source of external knowledge. In this paper, we propose CoT-KA, a Chain-of-Thought-based method that augments knowledge for deep learning. CoT-KA avoids the need for additional knowledge retrieval or knowledge reasoning models, as required in conventional augmentation methods. Our results demonstrate that CoT-KA outperforms both pure CoT-based methods and the non-augmented method across the majority of eleven publicly available benchmarks for various reasoning tasks.

Key facts. Cited by 21 papers 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 26 of its 34 references that are in the map. First posted on arXiv 4 Jul 2023.

Builds on

26 of 34 references.

Closest ancestors · 4

Same direction, newest first

  1. 2022Automatic Chain of Thought Prompting in Large Language Models7 Oct
  2. 2022Large Language Models are Zero-Shot Reasoners24 May
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  8. 2023A Survey of Large Language Modelscited by 2,537
  9. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  10. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,793
  11. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,219
  12. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
10 more
  1. 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,065
  2. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  3. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  4. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  5. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 642
  6. 2016Solving General Arithmetic Word Problemscited by 353
  7. 2020ReClor: A Reading Comprehension Dataset Requiring Logical Reasoningcited by 211
  8. 2021Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Textcited by 46
  9. 2022Knowledge-augmented Deep Learning and Its Applications: A Surveycited by 10
  10. 2023GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generationcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 12; 2025: 5; 2026: 1.

21 papers.

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  1. 2024Iterative Reasoning Preference Optimizationcited by 244
  2. 2024Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routingcited by 43
  3. 2024FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysiscited by 35
  4. 2023Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationalescited by 33
  5. 2024Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Modelscited by 20
  6. 2024Towards the Pedagogical Steering of Large Language Models for Tutoring: A Case Study with Modeling Productive Failurecited by 20
  7. 2024SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Modelscited by 7
  8. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generationcited by 5
  9. 2024AIPO: Improving Training Objective for Iterative Preference Optimizationcited by 5
  10. 2024Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloudcited by 4
  11. 2025CDW-CoT: Clustered Distance-Weighted Chain-of-Thoughts Reasoningcited by 3
  12. 2025Modeling the One-to-Many Property in Open-Domain Dialogue with LLMscited by 3
9 more
  1. 2023SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twittercited by 2
  2. 2024SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Eventscited by 2
  3. 2025TOD-ProcBench: Benchmarking Complex Instruction-Following in Task-Oriented Dialoguescited by 2
  4. 2025Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detectioncited by 2
  5. 2023Answering Unseen Questions With Smaller Language Models Using Rationale Generation and Dense Retrievalcited by 1
  6. 2024ACCORD: Closing the Commonsense Measurability Gapcited by 1
  7. 2025mSCoRe: a Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoningcited by 1
  8. 2024Towards Automated Solution Recipe Generation for Industrial Asset Management with LLMcited by 0
  9. 2026LEAF-SQL: Level-wise Exploration with Adaptive Fine-graining for Text-to-SQL Skeleton Predictioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024KAM-CoT: Knowledge Augmented Multimodal Chain-of-Thoughts Reasoning
  2. 2024Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis
  3. 2025CoT Vectors: Transferring and Probing the Reasoning Mechanisms of LLMs
  4. 2023Boosting Language Models Reasoning with Chain-of-Knowledge Prompting
  5. 2025Effectiveness of Chain-of-Thought in Distilling Reasoning Capability from Large Language Models
  6. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
  7. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
  8. 2023Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data