Introduction to the LLM-Planning-Papers Project
The LLM-Planning-Papers initiative is a comprehensive curation of essential research papers focusing on the planning capabilities of Large Language Models (LLMs). These cutting-edge studies explore how these sophisticated AI models can perform complex planning tasks, push the boundaries of artificial intelligence, and integrate seamlessly with embodied agents and other applications.
Project Background
The world of AI has witnessed remarkable progress, and Large Language Models have been at the forefront. LLM-Planning-Papers was created to address the growing interest in understanding how these models can improve and innovate planning capabilities. The project assembles key research papers, making it an indispensable resource for scholars, researchers, and enthusiasts interested in AI's planning realm.
Key Papers and Highlights
The project features a diverse range of research that delves into various aspects of planning with LLMs. Here are some notable works included in the list:
-
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents - This paper by Wenlong Huang and colleagues at the 2022 ICML conference examines how language models can act as zero-shot planners to harness actionable insights for embodied agents.
-
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models - Presented at ICLR 2023, this work by Denny Zhou et al., explores how prompting techniques can enhance the reasoning capabilities of large language models even for intricate tasks.
-
On Grounded Planning for Embodied Tasks with Language Models - This paper from AAAI 2023 by Bill Yuchen Lin and collaborators investigates how language models can engage in grounded planning to perform embodied tasks efficiently.
-
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models - At ICCV 2023, researchers Chan Hee Song and colleagues introduced LLM-Planner, showing how few-shot learning can enable grounded planning.
-
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models - Featured at ACL 2023.5, Lei Wang's team proposed techniques to enhance the zero-shot reasoning potential in LLMs through structured prompting.
Purpose and Impact
The project's mission is to centralize the most pivotal research on LLM planning abilities, offering a repository that reflects the innovation and development within AI planning. By providing a wide array of studies, it not only showcases the current state of research but also nurtures future advancements in the field.
Moreover, it highlights the various uses of LLMs in areas such as complex reasoning, task automation, interactive planning, and more, making them instrumental in a multitude of settings, including robotics, autonomous systems, and decision-making processes.
Future Directions
Continuously updated, LLM-Planning-Papers promises to keep pace with emerging breakthroughs and methodologies. The project is poised to include more recent developments and expand its influence by promoting a deeper understanding of how LLMs can redefine planning paradigms.
This initiative remains open to feedback and collaboration, inviting experts and the broader research community to contribute to its growth. As it evolves, LLM-Planning-Papers is set to become an enduring pillar of knowledge inspiring innovation and cross-disciplinary exploration in AI.
In summary, LLM-Planning-Papers is an essential compilation that captures the essence and evolution of planning abilities in large language models, providing a vital platform for researchers and practitioners aiming to push the boundaries of what AI can achieve in the realm of planning and beyond.