  Large language models (LLMs) outperform information retrieval techniques for
downstream knowledge-intensive tasks when being prompted to generate world
knowledge. However, community concerns abound regarding the factuality and
potential implications of using this uncensored knowledge. In light of this, we
introduce CONNER, a COmpreheNsive kNowledge Evaluation fRamework, designed to
systematically and automatically evaluate generated knowledge from six
important perspectives -- Factuality, Relevance, Coherence, Informativeness,
Helpfulness and Validity. We conduct an extensive empirical analysis of the
generated knowledge from three different types of LLMs on two widely studied
knowledge-intensive tasks, i.e., open-domain question answering and
knowledge-grounded dialogue. Surprisingly, our study reveals that the
factuality of generated knowledge, even if lower, does not significantly hinder
downstream tasks. Instead, the relevance and coherence of the outputs are more
important than small factual mistakes. Further, we show how to use CONNER to
improve knowledge-intensive tasks by designing two strategies: Prompt
Engineering and Knowledge Selection. Our evaluation code and LLM-generated
knowledge with human annotations will be released to facilitate future
research.
