  This study focuses on the evaluation of the Open Question Answering (Open-QA)
task, which can directly estimate the factuality of large language models
(LLMs). Current automatic evaluation methods have shown limitations, indicating
that human evaluation still remains the most reliable approach. We introduce a
new task, Evaluating QA Evaluation (QA-Eval) and the corresponding dataset
EVOUNA, designed to assess the accuracy of AI-generated answers in relation to
standard answers within Open-QA. Our evaluation of these methods utilizes
human-annotated results to measure their performance. Specifically, the work
investigates methods that show high correlation with human evaluations, deeming
them more reliable. We also discuss the pitfalls of current methods and methods
to improve LLM-based evaluators. We believe this new QA-Eval task and
corresponding dataset EVOUNA will facilitate the development of more effective
automatic evaluation tools and prove valuable for future research in this area.
All resources are available at \url{https://github.com/wangcunxiang/QA-Eval}
and it is under the Apache-2.0 License.
