WebWalker provides developers with a standardized evaluation process:
- Data preparation: Download the WebWalkerQA dataset (with 15,000+ labeled samples) containing sequences of web page actions and expected results. Execution
wget https://github.com/Alibaba-NLP/WebAgent/raw/main/dataset/webwalkerqa.jsonlGet. - test execution: Run
python evaluate_webwalker.py --dataset webwalkerqa.jsonl --model YOUR_MODEL_PATHThe -split parameter supports customizing the subset of tests (specify train/val/test with the -split parameter). - Analysis of indicators: The report outputs three core indicators:
- Navigation accuracy (ability to find the target page)
- Operational efficiency (average number of steps)
- Information extraction F1 value
- Comparison of results: WebWalker has built-in benchmark data for the SOTA model (including the GPT-4 fine-tuned version), which developers can compare side-by-side with the -benchmark parameter.
Advanced Usage: By modifying the webwalker/envs/custom_env.py Specific site structures can be simulated, or adversarial test cases can be injected to enhance model robustness.
This answer comes from the articleWebAgent: An Intelligent Web Information Search and Processing ToolThe
May not be reproduced without permission:AI productivity tools " How can developers utilize WebWalker for model performance testing?
