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.

介绍了 Eval4NLP 2023 共享任务，要求参与者在机器翻译和摘要评估中探索提示和分数提取，并评估了参与者的方法。在没有 fine-tuning 的限制下，最佳系统的表现与使用更大模型开发的最新的无参考度量标准（包括 GEMBA 和 Comet-Kiwi-XXL）相媲美甚至超过，并对 LLMs 的解释的可行性进行了小规模人类评估。