"Agentic Frameworks" introduces the concept of large language models (LLMs) as agents representing substantial progress in AI technology capabilities. These large language models overcome their previous language processing boundaries by acquiring the ability to work with environments, take action while making decisions, and learn from experience. The change requires LLMs to operate within distinct environments with different learning potentials and operational hurdles. The three types of environments include simulated worlds, web-based environments, and embodied environments. LLMs perform three activities within simulated worlds: knowledge graph navigation, simulated system interaction, and lateral thinking-based puzzle completion. The internet connectivity of web-based environments enables LLMs to browse websites to obtain data that helps them complete online forms. The embodied virtual environment represented by Minecraft allows LLMs to understand game mechanics while interacting with objects and working alongside different artificial agents.
Please watch this video to understand more about this topic: https://youtu.be/qI1fmFN-Zlo?si=wuP2m3O0dlxMgpv5
The video demonstrates how LLMs show outstanding capabilities to learn from these environments. The data processing abilities of LLMs show rapid environmental adjustment capability that leads to efficiently reaching challenging targets at higher speeds than earlier AI systems. The Voyager paper featured in the video demonstrates how LLMs learn Minecraft rapidly, enabling the task of building a diamond pickaxe at an unprecedented speed. The transformation of LLMs into agent systems faces numerous restrictions during their development process. The video identifies hallucination, together with generalization, as significant limitations that affect LLMs. The process of producing incorrect or illogical information that results from insufficient cognitive map development is known as hallucination. The internal conceptual frameworks that LLMs construct serve as mental representations to recognize patterns, logical rules, and cause-effect interactions. The challenges of generalized knowledge function as another barrier since LLMs find it hard to move skills learned in one context to other applications. The restriction demonstrates how hard it is for LLMs to extend knowledge obtained from one domain to perform tasks outside it.
These breakthroughs have extensive consequences for several domains, including medical care, educational institutions, artistic fields, pharmaceutical inventions, material development, and climate prediction methods. The capability of LLMs to work with humans as collaborators and partners creates the potential to transform business sectors and build new paths for both work creation and scientific advancement.
Utilize large language model-based agents within an agentic framework by integrating cognitive architectures, knowledge graphs, and reinforcement learning to enable autonomous decision-making and adaptive behavior.
IMO Agentic Frameworks represent a significant evolution in AI, where Large Language Models (LLMs) transition from primarily language processing tools to active agents capable of interacting with environments, making decisions, and learning from experience. This shift necessitates that LLMs operate within diverse environments, each presenting unique learning opportunities and challenges. These environments can be categorized as simulated worlds (where LLMs navigate knowledge graphs, interact with simulated systems, and solve puzzles), web-based environments (allowing LLMs to browse websites and complete online forms), and embodied environments (like Minecraft, where LLMs learn game mechanics, interact with objects, and collaborate with other agents).
The video referenced highlights LLMs' impressive ability to learn within these environments, demonstrating rapid adaptation and efficient goal achievement, surpassing previous AI systems. For example, the Voyager project showcases how an LLM quickly masters Minecraft, achieving complex tasks like building a diamond pickaxe at unprecedented speeds.
However, transforming LLMs into agents is not without limitations. Hallucination, the generation of incorrect or nonsensical information due to incomplete cognitive map development, is a significant hurdle. Cognitive maps, the internal conceptual frameworks LLMs build, are crucial for recognizing patterns, understanding logical rules, and grasping cause-effect relationships. Generalization, or the difficulty in transferring skills learned in one context to another, presents another challenge, hindering LLMs' ability to apply knowledge beyond their initial training domain.
Despite these limitations, the advancements in agentic LLMs have far-reaching implications across various sectors, including healthcare, education, creative industries, drug discovery, materials science, and climate modeling. The potential for LLMs to collaborate with humans as partners promises to transform industries, create new job opportunities, and accelerate scientific progress.
Thanks Friday Ameh and Touhidul Alam Seyam for sharing your valuable insgihts.
Hi Saikat Barua,
The concept of "Agentic Frameworks" for LLMs is indeed fascinating. The Voyager project showcases LLMs' ability to learn and adapt in complex environments like Minecraft, achieving milestones up to 15.3 times faster than previous methods. However, challenges like hallucinations where LLMs generate incorrect information remain significant, with some models exhibiting a 15% to 20% occurrence rate. Addressing these issues is crucial for the effective integration of LLMs across various sectors, including healthcare and education.
Thanks Muhammad Ehsan for sharing your valuable insights! Agentic Framework is still in its infancy, and a lot of things are needed to improved. It's a good practice to always add a verifier component/agent in your workflow to reduce hallucinations.
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