Overview
Verification of LLM output
Contact: arne.strickmann@code.berlin š¤ Large Language Models (LLMs) are powerful tools but produce hallucinations too often āinformation that isn't accurate or grounded in facts. š To avoid relying on incorrect data from LLMs, it's essential to have a system for fact-checking AI outputs. You can input a statement or fact, and within seconds, have it verified for accuracy. ā This process involves cross-checking the information against multiple sources and using different verification models, ensuring you can trust the final result. Using reliable fact-checking methods helps researchers, students, and professionals ensure they aren't misled by AI hallucinations, supporting more informed decisions based on trustworthy data.
Details
- Version1.3.2
- UpdatedOctober 11, 2024
- Size8.35MiB
- LanguagesEnglish
- Developer
Email
arne.strickmann@code.berlin - Non-traderThis developer has not identified itself as a trader. For consumers in the European Union, please note that consumer rights do not apply to contracts between you and this developer.
Privacy
This developer declares that your data is
- Not being sold to third parties, outside of the approved use cases
- Not being used or transferred for purposes that are unrelated to the item's core functionality
- Not being used or transferred to determine creditworthiness or for lending purposes
