OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
- URL: http://arxiv.org/abs/2305.16334v1
- Date: Tue, 23 May 2023 09:36:51 GMT
- Title: OlaGPT: Empowering LLMs With Human-like Problem-Solving Abilities
- Authors: Yuanzhen Xie, Tao Xie, Mingxiong Lin, WenTao Wei, Chenglin Li, Beibei
Kong, Lei Chen, Chengxiang Zhuo, Bo Hu, Zang Li
- Abstract summary: 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.
- Score: 19.83434949066066
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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: \url{https://github.com/oladata-team/OlaGPT}.
Related papers
- Advancing Reasoning in Large Language Models: Promising Methods and Approaches [0.0]
Large Language Models (LLMs) have succeeded remarkably in various natural language processing (NLP) tasks.
Their ability to perform complex reasoning-spanning logical deduction, mathematical problem-solving, commonsense inference, and multi-step reasoning-often falls short of human expectations.
This survey provides a comprehensive review of emerging techniques enhancing reasoning in LLMs.
arXiv Detail & Related papers (2025-02-05T23:31:39Z) - CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning [0.8192907805418583]
Chain-of-Associated-Thoughts (CoAT) framework introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed 'associative memory'
By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time.
These experiments demonstrated that our framework outperforms conventional inference processes on accuracy, coherence, and diversity.
arXiv Detail & Related papers (2025-02-04T15:10:33Z) - The Superalignment of Superhuman Intelligence with Large Language Models [63.96120398355404]
We discuss the concept of superalignment from the learning perspective to answer this question.
We highlight some key research problems in superalignment, namely, weak-to-strong generalization, scalable oversight, and evaluation.
We present a conceptual framework for superalignment, which consists of three modules: an attacker which generates adversary queries trying to expose the weaknesses of a learner model; a learner which will refine itself by learning from scalable feedbacks generated by a critic model along with minimal human experts; and a critic which generates critics or explanations for a given query-response pair, with a target of improving the learner by criticizing.
arXiv Detail & Related papers (2024-12-15T10:34:06Z) - Mimicking Human Intuition: Cognitive Belief-Driven Q-Learning [5.960184723807347]
We propose Cognitive Belief-Driven Q-Learning (CBDQ), which integrates subjective belief modeling into the Q-learning framework.
CBDQ enhances decision-making accuracy by endowing agents with human-like learning and reasoning capabilities.
We evaluate the proposed method on discrete control benchmark tasks in various complicate environments.
arXiv Detail & Related papers (2024-10-02T16:50:29Z) - Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning [0.0]
Large Language Models (LLMs) have demonstrated their capabilities across various tasks.
This paper exploits the reasoning and generative capabilities of the LLMs to predict human behavior in two sequential decision-making tasks.
We compare the performance of LLMs with a cognitive instance-based learning model, which imitates human experiential decision-making.
arXiv Detail & Related papers (2024-07-12T14:13:06Z) - Coding for Intelligence from the Perspective of Category [66.14012258680992]
Coding targets compressing and reconstructing data, and intelligence.
Recent trends demonstrate the potential homogeneity of these two fields.
We propose a novel problem of Coding for Intelligence from the category theory view.
arXiv Detail & Related papers (2024-07-01T07:05:44Z) - Igniting Language Intelligence: The Hitchhiker's Guide From
Chain-of-Thought Reasoning to Language Agents [80.5213198675411]
Large language models (LLMs) have dramatically enhanced the field of language intelligence.
LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer.
Recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents.
arXiv Detail & Related papers (2023-11-20T14:30:55Z) - From Heuristic to Analytic: Cognitively Motivated Strategies for
Coherent Physical Commonsense Reasoning [66.98861219674039]
Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions.
Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
arXiv Detail & Related papers (2023-10-24T19:46:04Z) - Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs [55.66353783572259]
Causal-Consistency Chain-of-Thought harnesses multi-agent collaboration to bolster the faithfulness and causality of foundation models.
Our framework demonstrates significant superiority over state-of-the-art methods through extensive and comprehensive evaluations.
arXiv Detail & Related papers (2023-08-23T04:59:21Z) - Confounder Identification-free Causal Visual Feature Learning [84.28462256571822]
We propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders.
CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions.
We uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective.
arXiv Detail & Related papers (2021-11-26T10:57:47Z) - Interpretable Reinforcement Learning Inspired by Piaget's Theory of
Cognitive Development [1.7778609937758327]
This paper entertains the idea that theories such as language of thought hypothesis (LOTH), script theory, and Piaget's cognitive development theory provide complementary approaches.
The proposed framework can be viewed as a step towards achieving human-like cognition in artificial intelligent systems.
arXiv Detail & Related papers (2021-02-01T00:29:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.