A Prefrontal Cortex-inspired Architecture for Planning in Large Language
Models
- URL: http://arxiv.org/abs/2310.00194v3
- Date: Wed, 6 Mar 2024 03:24:45 GMT
- Title: A Prefrontal Cortex-inspired Architecture for Planning in Large Language
Models
- Authors: Taylor Webb, Shanka Subhra Mondal, Chi Wang, Brian Krabach, Ida
Momennejad
- Abstract summary: Large language models (LLMs) often struggle with tasks that require multi-step reasoning or goal-directed planning.
We take inspiration from the human brain, in which planning is accomplished via the recurrent interaction of specialized modules in the prefrontal cortex (PFC)
- Score: 16.475564538598768
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) demonstrate impressive performance on a wide
variety of tasks, but they often struggle with tasks that require multi-step
reasoning or goal-directed planning. To address this, we take inspiration from
the human brain, in which planning is accomplished via the recurrent
interaction of specialized modules in the prefrontal cortex (PFC). These
modules perform functions such as conflict monitoring, state prediction, state
evaluation, task decomposition, and task coordination. We find that LLMs are
sometimes capable of carrying out these functions in isolation, but struggle to
autonomously coordinate them in the service of a goal. Therefore, we propose a
black box architecture with multiple LLM-based (GPT-4) modules. The
architecture improves planning through the interaction of specialized
PFC-inspired modules that break down a larger problem into multiple brief
automated calls to the LLM. We evaluate the combined architecture on three
challenging planning tasks -- graph traversal, Tower of Hanoi, and logistics --
finding that it yields significant improvements over standard LLM methods
(e.g., zero-shot prompting, in-context learning, and chain-of-thought). These
results demonstrate the benefit of utilizing knowledge from cognitive
neuroscience to improve planning in LLMs.
Related papers
- Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models [6.860460230412773]
We propose an LLM-embodied path planning framework for mobile agents.
Our proposed multi-layer architecture uses prompted LLMs in the path planning phase and integrates them with the mobile agents' low-level actuators.
Our experiments show that this framework can improve LLMs' 2D plane reasoning abilities and complete coverage path planning tasks.
arXiv Detail & Related papers (2024-07-02T12:38:46Z) - Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks.
LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning.
We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
arXiv Detail & Related papers (2024-06-20T13:08:09Z) - Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs [59.76268575344119]
We introduce a novel framework for enhancing large language models' (LLMs) planning capabilities by using planning data derived from knowledge graphs (KGs)
LLMs fine-tuned with KG data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval.
arXiv Detail & Related papers (2024-06-20T13:07:38Z) - From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems [59.40480894948944]
Large language model (LLM) empowered agents are able to solve decision-making problems in the physical world.
Under this model, the LLM Planner navigates a partially observable Markov decision process (POMDP) by iteratively generating language-based subgoals via prompting.
We prove that the pretrained LLM Planner effectively performs Bayesian aggregated imitation learning (BAIL) through in-context learning.
arXiv Detail & Related papers (2024-05-30T09:42:54Z) - A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models [15.874604623294427]
Multi-Phases planning problem involves multiple interconnected stages, such as outlining, information gathering, and planning.
Existing reasoning approaches have struggled to effectively address this complex task.
Our research aims to address this challenge by developing a human-like planning framework for LLM agents.
arXiv Detail & Related papers (2024-05-28T14:13:32Z) - Towards Efficient LLM Grounding for Embodied Multi-Agent Collaboration [70.09561665520043]
We propose a novel framework for multi-agent collaboration that introduces Reinforced Advantage feedback (ReAd) for efficient self-refinement of plans.
We provide theoretical analysis by extending advantage-weighted regression in reinforcement learning to multi-agent systems.
Experiments on Over-AI and a difficult variant of RoCoBench show that ReAd surpasses baselines in success rate, and also significantly decreases the interaction steps of agents.
arXiv Detail & Related papers (2024-05-23T08:33:19Z) - Sub-goal Distillation: A Method to Improve Small Language Agents [21.815417165548187]
Large Language Models (LLMs) have demonstrated significant promise as agents in interactive tasks.
We propose a method for transferring the performance of an LLM with billions of parameters to a much smaller language model.
In ScienceWorld, a challenging and multi-task interactive text environment, our method surpasses standard imitation learning based solely on elementary actions by 16.7%.
arXiv Detail & Related papers (2024-05-04T20:34:06Z) - LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning [78.2390460278551]
Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation.
Here, we present LLM3, a novel Large Language Model (LLM)-based TAMP framework featuring a domain-independent interface.
Specifically, we leverage the powerful reasoning and planning capabilities of pre-trained LLMs to propose symbolic action sequences and select continuous action parameters for motion planning.
arXiv Detail & Related papers (2024-03-18T08:03:47Z) - Small LLMs Are Weak Tool Learners: A Multi-LLM Agent [73.54562551341454]
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs.
We propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability.
arXiv Detail & Related papers (2024-01-14T16:17:07Z) - Dynamic Planning with a LLM [15.430182858130884]
Large Language Models (LLMs) can solve many NLP tasks in zero-shot settings, but applications involving embodied agents remain problematic.
Our work presents LLM Dynamic Planner (LLM-DP), a neuro-symbolic framework where an LLM works hand-in-hand with a traditional planner to solve an embodied task.
arXiv Detail & Related papers (2023-08-11T21:17:13Z)
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.