Summary of Tur[k]ingbench: a Challenge Benchmark For Web Agents, by Kevin Xu et al.
Tur[k]ingBench: A Challenge Benchmark for Web Agents
by Kevin Xu, Yeganeh Kordi, Tanay Nayak, Adi Asija, Yizhong Wang, Kate Sanders, Adam Byerly, Jingyu Zhang, Benjamin Van Durme, Daniel Khashabi
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether advanced multi-modal models can excel at complex web-based tasks typically found on crowdsourcing platforms. To address this question, the authors propose a novel approach that leverages the strengths of various AI techniques to tackle these challenges. The proposed method combines computer vision, natural language processing, and human-in-the-loop validation to improve performance on micro-tasks within web-based environments. This study aims to bridge the gap between traditional AI research and real-world applications by demonstrating the effectiveness of advanced multi-modal models in addressing complex web-based tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Can machines really help with complicated jobs online? Some websites ask people to do tiny tasks, like labeling images or summarizing text, within a web environment. This study explores whether super-smart computer models can handle these tough tasks effectively. The researchers came up with a new way to use AI techniques together to make progress on these micro-tasks. |
Keywords
» Artificial intelligence » Multi modal » Natural language processing
