Project Introduction: LLM-Agents-Papers
Description
The LLM-Agents-Papers project is an extensive collection of academic papers focused on agents that are based on Large Language Models (LLMs). Last updated on July 1, 2024, this repository serves as a comprehensive resource for anyone interested in the development, capabilities, and applications of LLM-based agents. The repository is systematically organized into various categories to facilitate easy access and understanding of the nuanced advancements in this field. Some of the key categories include:
- Surveys: Overviews and analyses of current trends and methodologies related to LLM-based agents.
- Planning: Papers focused on planning strategies and methodologies for LLM agents.
- Feedback & Reflection: Insights on improving LLM agent capabilities through feedback and self-reflection mechanisms.
- Memory Mechanism: Studies on the memory capacities and enhancements within LLM agents.
- Role Playing: Exploration of how LLM agents can engage in role-playing scenarios.
- Game Playing: Research on the application of LLM agents in gaming environments.
- Tool Usage & Human-Agent Interaction: Insights into how these agents interact with tools and humans.
- Benchmark & Evaluation: Methods and metrics for assessing the performance of LLM agents.
- Environment & Platform: Broader contexts and systems within which these agents function.
- Agent Framework: Structural guidelines for developing LLM-based agents.
- Multi-Agent System: Exploration of systems composed of multiple interacting LLM agents.
- Agent Fine-tuning: Techniques and methods for refining the performance of LLM agents.
Recommendations
For readers seeking a broader understanding or additional insights, LLM-Agents-Papers also recommends several other curated paper lists:
- zjunlp/LLMAgentPapers: A collection of must-read papers on LLM agents.
- teacherpeterpan/self-correction-llm-papers: Focused on self-correcting language models and automated feedback mechanisms.
- Paitesanshi/LLM-Agent-Survey: A comprehensive survey on autonomous LLM-based agents.
- woooodyy/llm-agent-paper-list: Another must-read collection covering a wide range of LLM-based agents.
- git-disl/awesome-LLM-game-agent-papers: Essential reading materials for game-oriented LLM agents.
Papers
The repository contains an annotated list of papers sorted into the thematic categories listed above. Here’s a closer look at some of these areas:
Survey
The survey category contains papers that review various aspects of LLM agents, including their workflows and common components. These papers are essential for understanding the strategic and technical landscape of LLM-based agents.
Planning
Documents in the planning section delve into how LLMs can be effectively harnessed for planning tasks. Topics range from automatic prompt engineering to utilizing knowledge in strategic decision-making processes.
Feedback & Reflection
Feedback and reflection papers discuss how agents can learn from interactions and feedback to refine their capabilities. This category emphasizes methods for self-correction and learning.
Memory Mechanism
Research papers in this section address the capacity of LLM agents to manage and utilize memory over long-term interactions, which is crucial for building more contextually aware and adaptive agents.
This project, through its structured approach, offers a rich repository of academic resources that are invaluable for researchers, developers, and enthusiasts interested in the rapidly evolving field of Large Language Model agents. Whether exploring foundational theories or cutting-edge applications, the LLM-Agents-Papers project is a central node for knowledge in this dynamic domain.