Factual Accuracy Assurance Program
DeepResearch uses a four-layer calibration mechanism to guard against modeling illusions:
1. Source verification
- set up
cross_verify=3Each argument is required to be at least:- Supported by 2 peer-reviewed papers
- 1 authoritative media report
- Automatic exclusion:
- Anonymous forum content
- Low Alexa ranking website
2. Timeline validation
- Dynamically generate the timeline (modify timeline.py):
- Marking key nodes in viewpoint evolution
- Detecting outdated theories (compare to the latest arXiv paper)
- Highlighting disciplinary milestones
3. Paradox detection
- start using
--debate_modeParameters:- Automatic generation of opposing viewpoints
- Calculating the proportion of evidence for and against
- Labeling the Focus of Controversy in Academia
4. Manual review interface
- pass (a bill or inspection etc)
api/reviewOffer:- Traceability of key passages (click on the quote to jump to the original page)
- Credibility scoring system (0-5 star rating)
- Expert annotation tool (required)
reviewer=yes)
Recommended for simultaneous deploymentWikiChatAs an auxiliary validation tool
This answer comes from the articleDeepResearch: a fully open source AI assistant for automated deep researchThe
May not be reproduced without permission:AI productivity tools " How can factual errors generated by open source models be prevented from affecting research conclusions?
