Researchers from China Introduce ControlLLM: An Artificial Intelligence Framework that Enables Large Language Models (LLMs) to Utilize Multi-Modal Tools for Solving Complex Real-World Task

The performance of LLMs in handling complex real-world tasks is impressive. However, there are cases where they may require assistance in using tools correctly due to vague user prompts, incorrect tool selection, and inadequate parameterisation and scheduling. To tackle these challenges, A group of researchers from The Hong Kong University of Science and Technology, OpenGVLab, Shanghai AI Laboratory, Tsinghua University and SenseTime proposes a ground-breaking framework called ControlLLM. The study aims to examine the significance of ControlLLM in enhancing the effectiveness of LLMs.

LLMs have made substantial strides in addressing planning, reasoning, and decision-making challenges for autonomous agents. Another avenue of study centres on augmenting LLMs with external tools to access current information, reduce hallucination, and enable multi-modal interactions. The tool-augmented LLMs leverage LLMs’ zero-shot or few-shot in-context learning to handle task decomposition, tool selection, and parameter completion without explicit fine-tuning. Challenges like hallucination and effective decomposition persist. Efforts are underway to cultivate LLMs with inherent multi-modal capabilities, expanding their applicability to more intricate real-world scenarios.

LLMs have demonstrated their prowess in natural language understanding, and they are now extending their capabilities to encompass multi-modal interactions. Tool-augmented LLMs seek to expand LLM functionality by incorporating tools that enable them to handle tasks involving images, videos, audio, and more despite the need to solve challenges such as task decomposition, tool selection, argument assignment, and efficient execution scheduling. Previous methods, such as Chain-of-Thought, Tree-of-Thought, and self-consistency, have addressed complex tasks by breaking them into smaller sub-tasks.

The ControlLLM framework comprises three essential components: a task decomposer, a Thoughts-on-Graph approach, and a versatile execution engine. The task decomposer breaks down complex user prompts into well-defined subtasks with distinct inputs and outputs. The Thoughts-on-Graph explores the best solution path on a predefined tool graph, specifying parameter and dependency relationships among tools. The execution engine interprets this path and efficiently executes actions across various computational devices.

The ControlLLM framework excels in accuracy, efficiency, and versatility compared to existing methods, particularly in various tasks encompassing image, audio, and video processing. It boasts an impressive 98% success rate in solution evaluation for challenging tasks, surpassing the best baseline performance at 59%. ControlLLM also significantly enhances tool usage, adeptly inferring and assigning tool arguments. In both simple and intricate scenarios, ControlLLM seamlessly integrates various information types to generate comprehensive and meaningful responses based on execution outcomes.

In conclusion, the ControlLLM framework empowers LLMs to employ multi-modal tools for tackling intricate real-world tasks, offering superior accuracy, efficiency, and adaptability. Its components, including a task decomposer, Thoughts-on-Graph methodology, and a versatile execution engine, collectively contribute to substantial improvements in tool utilisation. ControlLLM consistently demonstrates its prowess by expertly inferring and assigning tool arguments and attaining a high success rate in solution evaluations. Through extensive case studies, it reaffirms its task planning capabilities, delivering diverse solutions that enhance the user experience. ControlLLM integrates varied information sources to generate comprehensive and meaningful responses grounded in execution outcomes.

Check out the Paper and GithubAll credit for this research goes to the researchers of this project. Also, don’t forget to join our 32k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.

If you like our work, you will love our newsletter..

We are also on Telegram and WhatsApp.

Hello, My name is Adnan Hassan. I am a consulting intern at Marktechpost and soon to be a management trainee at American Express. I am currently pursuing a dual degree at the Indian Institute of Technology, Kharagpur. I am passionate about technology and want to create new products that make a difference.

🐝 Join the Fastest Growing AI Research Newsletter Read by Researchers from Google + NVIDIA + Meta + Stanford + MIT + Microsoft and many others...