Exploring Autonomous Agents through the Lens of Large Language Models: A Review
This is an incremental review paper summarizing existing advancements and challenges in using LLMs for autonomous agents, relevant for researchers and practitioners in AI.
The paper reviews how Large Language Models (LLMs) enable autonomous agents to perform diverse tasks across domains like customer service and healthcare, highlighting challenges such as multimodality and hallucinations, and discusses techniques like prompting and evaluation platforms to enhance capabilities.
Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. These agents, proficient in human-like text comprehension and generation, have the potential to revolutionize sectors from customer service to healthcare. However, they face challenges such as multimodality, human value alignment, hallucinations, and evaluation. Techniques like prompting, reasoning, tool utilization, and in-context learning are being explored to enhance their capabilities. Evaluation platforms like AgentBench, WebArena, and ToolLLM provide robust methods for assessing these agents in complex scenarios. These advancements are leading to the development of more resilient and capable autonomous agents, anticipated to become integral in our digital lives, assisting in tasks from email responses to disease diagnosis. The future of AI, with LLMs at the forefront, is promising.