A collection of some awesome public projects about LLM-based Web Agents and Tools (Continuously Update...).
(The following section was automatically generated by ChatGPT)
An LLM-based Web Agent can accomplish a variety of text-based tasks such as writing and proofreading articles, analyzing snippets of code, or even conducting advanced dialogues. Its purposes can range from personal assistance (scheduling, reminders, and searching for information) to professional tasks (technical analysis, summarizing reports, acting as a customer service representative, among other things).
The benefits of using LLMs as Web Agents include:
- They can operate 24/7, providing continuous support.
- They can handle a large volume of requests and responses.
- They do not require breaks and cannot suffer from fatigue.
Despite these benefits, it's crucial to remember that they can't replace human judgement in sensitive or critical matters. They are most suitable for automating repetitive tasks, assisting in information extraction and providing guidance on widely covered topics.
To design a Web Agent using a Large Language Model, the following steps may be considered:
- Define the tasks: Clearly specify the tasks the LLM-based Web Agent should perform.
- Data Gathering and Training: Gather the relevant data, and train the LLM to perform the tasks it needs to accomplish.
- Integration: Integrate the trained model into your web application.
- Testing and Iteration: Conduct regular testing and improve the model for better performance.
- Monitoring and Updating: Constantly monitor the model's performance and regularly update or re-train the model to adapt to changes.
format:
- [title](paper link) [links]
- author1, author2, and author3...
- publisher
- keyword
- code
- experiment environments and datasets
- SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
- Kanzhi Cheng, Qiushi Sun, Yougang Chu, Fangzhi Xu, Yantao Li, Jianbing Zhang, Zhiyong Wu
- Keyword: Large Language Model, Visual GUI Agents
- Code: official
format:
- [title](codebase link) [links]
- author1, author2, and author3...
- keyword
- experiment environments, datasets or tasks
format:
- [title](benchmark link) [links]
- author1, author2, and author3...
- keyword
- experiment environments or tasks
format:
- [title](tool link) [links]
- author1, author2, and author3...
- keyword
- experiment environments, datasets or tasks
-
- Meta
- Large Language Model
- Huggingface Site: official
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- Shenzhi-Wang
- Large Language Model, Chinese chat fine-tuned
- Huggingface Site: official
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- 中国联通AI创新中心
- Large Language Model, Chinese chat fine-tuned
- Huggingface Site: official
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- Skyvern-AI
- Automate browser-based workflows with LLMs and Computer Vision
- Huggingface Site: official