mlsanityData to 7 Oct 2026

OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities

arXiv 2305.16334 ↗First posted 23 May 2023cs.CL cs.AIcited by 10

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

In most current research, large language models (LLMs) are able to perform reasoning tasks by generating chains of thought through the guidance of specific prompts. However, there still exists a significant discrepancy between their capability in solving complex reasoning problems and that of humans. At present, most approaches focus on chains of thought (COT) and tool use, without considering the adoption and application of human cognitive frameworks. It is well-known that when confronting complex reasoning challenges, humans typically employ various cognitive abilities, and necessitate interaction with all aspects of tools, knowledge, and the external environment information to accomplish intricate tasks. This paper introduces a novel intelligent framework, referred to as OlaGPT. OlaGPT carefully studied a cognitive architecture framework, and propose to simulate certain aspects of human cognition. The framework involves approximating different cognitive modules, including attention, memory, reasoning, learning, and corresponding scheduling and decision-making mechanisms. Inspired by the active learning mechanism of human beings, it proposes a learning unit to record previous mistakes and expert opinions, and dynamically refer to them to strengthen their ability to solve similar problems. The paper also outlines common effective reasoning frameworks for human problem-solving and designs Chain-of-Thought (COT) templates accordingly. A comprehensive decision-making mechanism is also proposed to maximize model accuracy. The efficacy of OlaGPT has been stringently evaluated on multiple reasoning datasets, and the experimental outcomes reveal that OlaGPT surpasses state-of-the-art benchmarks, demonstrating its superior performance. Our implementation of OlaGPT is available on GitHub: urlhttps://github.com/oladata-team/OlaGPT.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 22 of its 35 references that are in the map. First posted on arXiv 23 May 2023.

Builds on

22 of 35 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023LLaMA: Open and Efficient Foundation Language Models27 Feb
  2. 2023Augmented Language Models: a Survey15 Feb
  3. 2022PAL: Program-aided Language Models18 Nov
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Models6 Oct
  5. 2022E-KAR: A Benchmark for Rationalizing Natural Language Analogical Reasoning16 Mar

Other references · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  7. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  8. 2017Billion-scale similarity search with GPUscited by 2,477
  9. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 895
  10. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 684
  11. 2017Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problemscited by 642
  12. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
5 more
  1. 2023ART: Automatic multi-step reasoning and tool-use for large language modelscited by 193
  2. 2023Active Prompting with Chain-of-Thought for Large Language Modelscited by 154
  3. 2023Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Datacited by 119
  4. 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
  5. 2022SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Modelscited by 38

Built on by

Cited by year

Citing papers per year: 2023: 5; 2024: 2; 2025: 3.

10 papers.

  1. 2023Cognitive Architectures for Language Agentscited by 422
  2. 2023Lyfe Agents: Generative agents for low-cost real-time social interactionscited by 49
  3. 2023Holistic Evaluation of GPT-4V for Biomedical Imagingcited by 19
  4. 2023Synergistic Integration of Large Language Models and Cognitive Architectures for Robust AI: An Exploratory Analysiscited by 11
  5. 2025Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experiencecited by 7
  6. 2024Enhancing LLMs for Physics Problem-Solving using Reinforcement Learning with Human-AI Feedbackcited by 6
  7. 2023AutoScrum: Automating Project Planning Using Large Language Modelscited by 1
  8. 2025A Toolbox, Not a Hammer -- Multi-TAG: Scaling Math Reasoning with Multi-Tool Aggregationcited by 1
  9. 2024Augmenting Lateral Thinking in Language Models with Humor and Riddle Data for the BRAINTEASER Taskcited by 0
  10. 2025Enhancing Computational Cognitive Architectures with LLMs: A Case Studycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism
  2. 2025Cognitive Foundations for Reasoning and Their Manifestation in LLMs
  3. 2025Eliciting Reasoning in Language Models with Cognitive Tools
  4. 2025From System 1 to System 2: A Survey of Reasoning Large Language Models
  5. 2024Unlocking Structured Thinking in Language Models with Cognitive Prompting
  6. 2024Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology
  7. 2024How Likely Do LLMs with CoT Mimic Human Reasoning?
  8. 2025Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models