In this edition, we’ll explore tool use—arguably one of the hottest new capabilities of LLMs. We’ll look at types of tools, benefits of tool use, recent developments, and future directions.
Sebastian, 22 nov on Ai Journey conference we presented a multi-agent system FractalGPT and its Math agent, that is able to solve math tasks with 99.8% accuracy, outperforming most LLM nevertheless they are trained on math dataset or not (ChatGPT, GPT4, Claude2). Tested on dataset https://huggingface.co/datasets/ChilleD/MultiArith Very well scaled to any math problem
Sebastian, 22 nov on Ai Journey conference we presented a multi-agent system FractalGPT and its Math agent, that is able to solve math tasks with 99.8% accuracy, outperforming most LLM nevertheless they are trained on math dataset or not (ChatGPT, GPT4, Claude2). Tested on dataset https://huggingface.co/datasets/ChilleD/MultiArith Very well scaled to any math problem
Watch: https://aij.ru/eng/broadcast?date=2023-11-22&streamName=Science Hall 1
Autonomous AI agents: industry trends and why prompts are not almighty
Viktor Nosko
FractalTech
This agent approach is a successor of tool use.