IAAR-Shanghai/UHGEval
[ACL 2024] User-friendly evaluation framework: Eval Suite & Benchmarks: UHGEval, HaluEval, HalluQA, etc.
This tool helps researchers, product managers, and AI safety engineers evaluate how often Chinese Large Language Models (LLMs) generate incorrect or made-up information. It takes an LLM and a prompt as input, and then uses various methods to measure the 'hallucination rate' of the generated Chinese text. The output helps users understand how reliable specific Chinese LLMs are for tasks requiring factual accuracy.
180 stars. No commits in the last 6 months.
Use this if you need a comprehensive and user-friendly way to measure the factual accuracy and hallucination tendencies of Chinese LLMs across different benchmarks.
Not ideal if your primary concern is evaluating LLMs for English content or if you require fine-grained analysis beyond hallucination, such as toxicity or bias.
Stars
180
Forks
13
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 07, 2025
Commits (30d)
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