Sethuraman T V


2024

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WebWISE: Unlocking Web Interface Control for LLMs via Sequential Exploration
Heyi Tao | Sethuraman T V | Michal Shlapentokh-Rothman | Tanmay Gupta | Heng Ji | Derek Hoiem
Findings of the Association for Computational Linguistics: NAACL 2024

This paper investigates using Large Language Models (LLMs) to automatically perform web software tasks using click, scroll, and text in- put 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 our proposed method on the MiniWob++ benchmark. With only one in-context example, our WebWISE method using gpt-3.5-turbo achieves similar or better performance than other methods that require many demonstrations or trials.