ExpeL: LLM Agents Are Experiential Learners
ExpeL enhances LLM decision-making by learning from experiences in natural language without parameter updates.
Key Findings
Methodology
ExpeL is an LLM agent that learns from experiences without parameter updates. It autonomously gathers experiences from training tasks and extracts knowledge using natural language. During inference, it recalls insights and past experiences to make informed decisions. This method emphasizes retaining experiences across multiple tasks to enhance agent performance.
Key Results
- ExpeL outperformed strong baselines in three domains with success rates of 40% in HotpotQA, 59% in ALFWorld, and 37% in WebShop.
- In cross-task learning, ExpeL matched Reflexion in HotpotQA and outperformed it in ALFWorld.
- ExpeL demonstrated potential for performance improvement through experience accumulation without parameter updates.
Significance
ExpeL provides a novel approach for applying LLMs in decision-making tasks, avoiding the resource-intensive parameter updates and potential loss of generalization. It shows the potential for improving model performance through experiential learning without accessing model weights, offering significant academic and industrial value.
Technical Contribution
ExpeL's technical contribution lies in its learning mechanism without parameter updates, contrasting with existing methods. It demonstrates new engineering possibilities through natural language extraction and application of experiences, offering new avenues for closed-source model applications.
Novelty
ExpeL is the first to propose an LLM agent that learns from experiences in natural language, differing from previous methods relying on parameter fine-tuning, providing a learning path without accessing model weights.
Limitations
- ExpeL may underperform in extremely complex tasks that require finer parameter adjustments.
- In scenarios requiring real-time feedback, ExpeL's experiential learning may not be fast enough.
Future Work
Future research could explore ExpeL's application in more domains, especially in scenarios requiring rapid adaptation to new tasks. Combining with other learning methods might further enhance its performance.
AI Executive Summary
Recent interest in applying large language models (LLMs) to decision-making tasks has highlighted the limitations of existing methods, which often require parameter fine-tuning, consuming resources and potentially reducing generalization.
ExpeL is an innovative LLM agent that autonomously gathers and extracts experiences in natural language, enhancing decision-making capabilities without parameter updates. Its core lies in accumulating experiences through trial and error and leveraging these during inference.
Experimental results show ExpeL outperforms existing baselines across multiple domains, demonstrating its potential to improve performance through experiential learning without accessing model weights. However, ExpeL still has room for improvement in handling extremely complex tasks, and future research could explore its application in more domains.
Deep Analysis
Background
The development of large language models (LLMs) has expanded their application in natural language processing and decision-making tasks. Existing methods often rely on parameter fine-tuning, which consumes resources and may reduce generalization. Researchers are now exploring learning methods that do not require parameter updates to overcome these challenges.
Core Problem
Existing LLMs face challenges of high resource consumption and reduced generalization in decision-making tasks. How to enhance model performance through experiential learning without accessing model weights is a pressing issue.
Innovation
ExpeL autonomously gathers and extracts experiences in natural language, enhancing decision-making capabilities without parameter updates. It accumulates experiences through trial and error and leverages these during inference, differing from previous methods relying on parameter fine-tuning.
Methodology
- �� Autonomous experience gathering: Accumulate success and failure experiences through trial and error.
- �� Knowledge extraction: Extract insights from experiences in natural language.
- �� Experience application: Utilize extracted insights and past experiences during inference to make decisions.
Experiments
ExpeL was tested in HotpotQA, ALFWorld, and WebShop domains. Evaluated using gpt-3.5-turbo-0613, it achieved success rates of 40%, 59%, and 37%, respectively. Results demonstrate ExpeL's potential to improve performance through experiential learning without accessing model weights.
Results
ExpeL matched Reflexion in HotpotQA and outperformed it in ALFWorld, demonstrating potential for performance improvement through experience accumulation without parameter updates.
Applications
ExpeL can be applied in scenarios requiring rapid adaptation to new tasks, such as autonomous driving and intelligent customer service. It offers new engineering possibilities through natural language extraction and application of experiences.
Limitations & Outlook
ExpeL may underperform in extremely complex tasks requiring finer parameter adjustments. Additionally, in scenarios requiring real-time feedback, ExpeL's experiential learning may not be fast enough.
Plain Language Accessible to non-experts
Imagine learning to ride a bike. Initially, you might fall a few times, but each time you learn something, like how to balance or turn. ExpeL is like a smart learner that accumulates experiences through trial and error and recalls these experiences when needed to make better decisions. It doesn't need to adjust any parts of the bike (like updating model parameters) but improves its riding skills through continuous practice and experience accumulation.
ELI14 Explained like you're 14
Hey there! Imagine playing a super complex game. Each time you fail, you note down why you failed and avoid the same mistake next time. ExpeL is like a super smart player that gets better by constantly trying and learning from experiences. It doesn't need to change the game rules but improves its skills through continuous practice. Isn't that cool?
Glossary
Large Language Model (LLM)
A neural network model capable of processing and generating natural language text.
Used for natural language processing and decision-making tasks.
Experiential Learning
A method of improving performance by learning from past experiences.
ExpeL extracts and applies experiences using natural language.
Parameter Fine-tuning
The process of adjusting model parameters to adapt to specific tasks.
Existing methods often rely on parameter fine-tuning.
Natural Language Processing
Technology for computers to understand and generate human language.
ExpeL uses natural language for experience extraction.
Transfer Learning
Applying knowledge learned from one task to another related task.
ExpeL demonstrates potential for cross-task learning.
Open Questions Unanswered questions from this research
- 1 How to effectively apply ExpeL in extremely complex tasks? Existing methods may require finer parameter adjustments.
- 2 How to enhance ExpeL's learning speed in scenarios requiring real-time feedback?
Applications
Immediate Applications
Intelligent Customer Service
Quickly adapts to different customer needs through experiential learning, providing personalized service.
Autonomous Driving
Enhances driving decision-making capabilities through experiential learning in various road conditions.
Long-term Vision
General Artificial Intelligence
Achieves broader task adaptability through experiential learning, advancing the development of artificial intelligence.
Abstract
The recent surge in research interest in applying large language models (LLMs) to decision-making tasks has flourished by leveraging the extensive world knowledge embedded in LLMs. While there is a growing demand to tailor LLMs for custom decision-making tasks, finetuning them for specific tasks is resource-intensive and may diminish the model's generalization capabilities. Moreover, state-of-the-art language models like GPT-4 and Claude are primarily accessible through API calls, with their parametric weights remaining proprietary and unavailable to the public. This scenario emphasizes the growing need for new methodologies that allow learning from agent experiences without requiring parametric updates. To address these problems, we introduce the Experiential Learning (ExpeL) agent. Our agent autonomously gathers experiences and extracts knowledge using natural language from a collection of training tasks. At inference, the agent recalls its extracted insights and past experiences to make informed decisions. Our empirical results highlight the robust learning efficacy of the ExpeL agent, indicating a consistent enhancement in its performance as it accumulates experiences. We further explore the emerging capabilities and transfer learning potential of the ExpeL agent through qualitative observations and additional experiments.