The Eval4NLP 2023 Shared Task on Prompting Large Language Models as
  Explainable Metrics
Christoph Leiter Juri Opitz Daniel Deutsch Yang Gao Rotem Dror Steffen Eger
With an increasing number of parameters and pre-training data, generative
large language models (LLMs) have shown remarkable capabilities to solve tasks
with minimal or no task-related examples. Notably, LLMs have been successfully
employed as evaluation metrics in text generation tasks. Within this context,
we introduce the Eval4NLP 2023 shared task that asks participants to explore
prompting and score extraction for machine translation (MT) and summarization
evaluation. Specifically, we propose a novel competition setting in which we
select a list of allowed LLMs and disallow fine-tuning to ensure a focus on
prompting. We present an overview of participants' approaches and evaluate them
on a new reference-free test set spanning three language pairs for MT and a
summarization dataset. Notably, despite the task's restrictions, the
best-performing systems achieve results on par with or even surpassing recent
reference-free metrics developed using larger models, including GEMBA and
Comet-Kiwi-XXL. Finally, as a separate track, we perform a small-scale human
evaluation of the plausibility of explanations given by the LLMs.