Optimize the LLM Action Planning Space w/ ICL (Google)
AI Summary
In this video titled “Optimize the LLM Action Planning Space w/ ICL (Google)” by Discover AI, the speaker delves into the concept of AI action and its relationship with In-Context Learning (ICL). Key points discussed include:
- The evolution of planning in AI, emphasizing the shift from propositional knowledge to procedural knowledge, which focuses on strategies and sequences for task execution.
- The introduction of key technical terms, including the Planning Domain Definition Language (PDDL) and ‘exemplars’ - worked-out examples used to teach LLMs.
- Insights from a recent study by Google DeepMind and Carnegie Mellon University, which proposes improving LLM planning by using action sequence similarity rather than solely relying on task descriptions.
- The necessity of selecting examples based on similarity of action sequences to enhance planning and reduce errors in LLM outputs.
- A discussion on dynamic clustering to enhance diversity and reduce redundancy in selected exemplars for better reasoning capabilities in models.
- Speculative thoughts on the future of AI reasoning and training, suggesting the potential for embedding procedural knowledge into vector spaces for improved operational efficiency.
This video aims to illuminate the practical applications of the latest AI research, highlighting how in-context learning can optimize action planning for agentic systems.
Description
New ai Action Planning via In-Context Learning (ICL): Planning is essential for artificial intelligence systems to look ahead and proactively determine a course of actions to reach objectives in the virtual and real world. ICL could prove to be real powerful instrument for improved Action sequence planning of agentic systems.
All rights w/ authors:
IMPROVING LARGE LANGUAGE MODEL PLANNING WITH ACTION SEQUENCE SIMILARITY
Xinran Zhao1,2∗, Hanie Sedghi1, Bernd Bohnet1, Dale Schuurmans1, Azade Nova1
from
1 Google DeepMind,
2 Carnegie Mellon University
scienceexplained
reasoning
aiagents
logicalreasoning
ailearning