Autonomous agents powered by large language models (LLMs) have garnered considerable research attention. However, the open-source community faces significant hurdles in developing specialized models for agent tasks, primarily due to the limited availability of high-quality datasets and the lack of standardized protocols in this field.
In a new paper xLAM: A Family of Large Action Models to Empower AI Agent Systems, a Salesforce AI Research team
presents the xLAM series, a collection of large action models designed to enhance the performance of open-source LLMs for autonomous AI agents. This work aims to accelerate innovation in the field and make high-performance models for agent tasks more accessible.