Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large
  Language Models on Sequence to Sequence Tasks
Andrea Sottana Bin Liang Kai Zou Zheng Yuan
Large Language Models (LLMs) evaluation is a patchy and inconsistent
landscape, and it is becoming clear that the quality of automatic evaluation
metrics is not keeping up with the pace of development of generative models. We
aim to improve the understanding of current models' performance by providing a
preliminary and hybrid evaluation on a range of open and closed-source
generative LLMs on three NLP benchmarks: text summarisation, text
simplification and grammatical error correction (GEC), using both automatic and
human evaluation. We also explore the potential of the recently released GPT-4
to act as an evaluator. We find that ChatGPT consistently outperforms many
other popular models according to human reviewers on the majority of metrics,
while scoring much more poorly when using classic automatic evaluation metrics.
We also find that human reviewers rate the gold reference as much worse than
the best models' outputs, indicating the poor quality of many popular
benchmarks. Finally, we find that GPT-4 is capable of ranking models' outputs
in a way which aligns reasonably closely to human judgement despite
task-specific variations, with a lower alignment in the GEC task.