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GPT-4V(ision) is a Generalist Web Agent, if Grounded
GPT-4V(ision) is a Generalist Web Agent, if Grounded
ratings:
Length:
43 minutes
Released:
Jan 9, 2024
Format:
Podcast episode
Description
The recent development on large multimodal models (LMMs), especially GPT-4V(ision) and Gemini, has been quickly expanding the capability boundaries of multimodal models beyond traditional tasks like image captioning and visual question answering. In this work, we explore the potential of LMMs like GPT-4V as a generalist web agent that can follow natural language instructions to complete tasks on any given website. We propose SEEACT, a generalist web agent that harnesses the power of LMMs for integrated visual understanding and acting on the web. We evaluate on the recent MIND2WEB benchmark. In addition to standard offline evaluation on cached websites, we enable a new online evaluation setting by developing a tool that allows running web agents on live websites. We show that GPT-4V presents a great potential for web agents - it can successfully complete 50% of the tasks on live websites if we manually ground its textual plans into actions on the websites. This substantially outperforms text-only LLMs like GPT-4 or smaller models (FLAN-T5 and BLIP-2) specifically fine-tuned for web agents. However, grounding still remains a major challenge. Existing LMM grounding strategies like set-of-mark prompting turns out not effective for web agents, and the best grounding strategy we develop in this paper leverages both the HTML text and visuals. Yet, there is still a substantial gap with oracle grounding, leaving ample room for further improvement.
2024: Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, Yu Su
https://arxiv.org/pdf/2401.01614v1.pdf
2024: Boyuan Zheng, Boyu Gou, Jihyung Kil, Huan Sun, Yu Su
https://arxiv.org/pdf/2401.01614v1.pdf
Released:
Jan 9, 2024
Format:
Podcast episode
Titles in the series (100)
Self-regulating Prompts: Foundational Model Adaptation without Forgetting: Prompt learning has emerged as an efficient alternative for fine-tuning foundational models, such as CLIP, for various downstream tasks. Conventionally trained using the task-specific objective, i.e., cross-entropy loss, prompts tend to overfit downs... by Papers Read on AI