Reframing Large Language Models (LLMs) as agents has ushered in a new paradigm of automation. Researchers and practitioners have increasingly been using these models as agents to automate complex tasks using specialized functions. However, integrating useful functions into LLM agents often requires manual effort and extensive iterations, which is time-consuming and inefficient. Inspired by the analogy of humans continuously forging tools to adapt to tasks, this paper introduces a novel approach to train LLM agents by forging their functions, treating them as learnable 'agent parameters', without modifying the LLM weights. This paradigm, termed 'Agent Training', involves updating the agent's functions to maximize task-solving ability, offering a promising avenue for developing specialized LLM agents efficiently. …
EcoAssistant, built on the principles outlined in the paper "EcoAssistant: Using LLM Assistant More Affordably and Accurately", showcases an advanced application of AutoGen in AI-driven question answering. The system's implementation hinges on three pivotal features: …
In the ever-evolving landscape of artificial intelligence, the recent paper "EcoAssistant: Using LLM Assistant More Affordably and Accurately" emerges as a groundbreaking study. This research paper delves into the complexities of utilizing Large Language Models (LLMs) in a cost-effective and accurate manner, specifically for code-driven question answering. This innovation builds on the capabilities of Autogen, a key component in enhancing the effectiveness of the model. …