PaSa
P
Pasa
Overview :
PaSa is an advanced academic paper search agent developed by ByteDance, based on large language model (LLM) technology. It can autonomously invoke search tools, read papers, and filter relevant references to obtain comprehensive and accurate results for complex academic queries. This technology is optimized through reinforcement learning, trained using the synthetic dataset AutoScholarQuery, and has shown outstanding performance on the real-world query dataset RealScholarQuery, significantly outperforming traditional search engines and GPT-based methods. The main advantages of PaSa lie in its high recall and precision rates, providing researchers with a more efficient academic search experience.
Target Users :
PaSa is designed for researchers, scholars, and students, particularly those who need an efficient way to locate and filter academic papers. It helps users quickly identify the most relevant research literature, saving time and effort while enhancing academic research efficiency.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 73.4K
Use Cases
Researchers use PaSa to quickly find the latest papers related to machine learning.
Students utilize PaSa to find high-quality references for their theses.
Research teams filter key research literature in specific fields through PaSa to accelerate project progress.
Features
Autonomously invoke search tools, generate search queries, and obtain relevant papers.
Read paper content and filter literature most relevant to user queries.
Expand search scope through citation networks to discover more related papers.
Support multi-round searches and filtering to progressively optimize results.
Provide search results with high recall and precision rates, significantly better than traditional methods.
How to Use
1. Visit the official PaSa website or use its API interface.
2. Enter detailed academic search requirements, such as research topics and keywords.
3. PaSa automatically invokes search tools and generates a list of relevant papers.
4. The system filters and ranks the papers, allowing users to view recommended results.
5. Adjust search parameters as needed to optimize results.
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