Realization of objectives and context
WebAgent, as an intelligent web information search tool, is designed for users who need to deal with a large amount of academic information. Its WebSailor component is especially good at multi-step complex task processing, which can significantly improve the efficiency of academic information access.
Specific steps
- Model Selection: Preferred WebSailor-72B model, which demonstrates the best performance in processing academic information
- Query Optimization: Input should include specific academic requirements, for example:
'Finding Papers in Natural Language Processing Accepted at ACL Conferences in 2025, Excluding Review Articles' - Results Screening: check search paths via logging to optimize keyword weights (adjusted in config.yaml)
- Automated processing: Configure API timed queries to automatically track updates by specific authors or research directions
Optimization Recommendations
Custom models can be trained using the SailorFog-QA dataset to optimize search performance for specific academic domains. Recommended to be paired with conda virtual environment to manage dependency packages for different research projects.
This answer comes from the articleWebAgent: An Intelligent Web Information Search and Processing ToolThe
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