WebWISE: Web Interface Control and Sequential Exploration with Large Language Models
This addresses the problem of inefficient training for web automation tasks, offering a more efficient approach for developers and researchers, though it is incremental over existing LLM methods.
The paper tackles automating web software tasks using a Large Language Model (LLM) to perform click, scroll, and text input operations, achieving similar or better performance than other methods on the MiniWob++ benchmark with only one in-context example.
The paper investigates using a Large Language Model (LLM) to automatically perform web software tasks using click, scroll, and text input operations. Previous approaches, such as reinforcement learning (RL) or imitation learning, are inefficient to train and task-specific. Our method uses filtered Document Object Model (DOM) elements as observations and performs tasks step-by-step, sequentially generating small programs based on the current observations. We use in-context learning, either benefiting from a single manually provided example, or an automatically generated example based on a successful zero-shot trial. We evaluate the proposed method on the MiniWob++ benchmark. With only one in-context example, our WebWISE method achieves similar or better performance than other methods that require many demonstrations or trials.