agent‐based systems - chunhualiao/public-docs GitHub Wiki

leture 6.

Basics

agent

An “intelligent” system that interacts with some “environment”
- Physical environments: robot, autonomous car, …
- Digital environments: DQN for Atari, Siri, AlphaGo, …
- Humans as environments: chatbot

Why LLM Agents?

Solving real-world tasks typically involves a trial-and-error process
Leveraging external tools and retrieving from external knowledge expand LLM’s capabilities
task decomposition: allocation of subtasks to specialized modules

List

Reasoning and planning: LLM agents tend to make mistakes when performing complex tasks end-to-end
Embodiment and learning from environment feedback
- LLM agents are not yet efficient at recovering from mistakes for long-horizon tasks
- Continuous learning, self-improvement
- Multimodal understanding, grounding and world models
safety and privacy: LLMs are susceptible to adversarial attacks, can emit harmful messages and leak private data
human-agent interaction, ethics: How to effectively control the LLM agent behavior, and design the interaction mode between humans and LLM agents