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With an increasing number of parameters and pre-training data, generativelarge language models (LLMs) have shown remarkable capabilities to solve taskswith minimal or no task-related examples. Notably, LLMs have been successfullyemployed as evaluation metrics in text generation tasks. Within this context,we introduce the Eval4NLP 2023 shared task that asks participants to exploreprompting and score extraction for machine translation (MT) and summarizationevaluation. Specifically, we propose a novel competition setting in which weselect a list of allowed LLMs and disallow fine-tuning to ensure a focus onprompting. We present an overview of participants' approaches and evaluate themon a new reference-free test set spanning three language pairs for MT and asummarization dataset. Notably, despite the task's restrictions, thebest-performing systems achieve results on par with or even surpassing recentreference-free metrics developed using larger models, including GEMBA andComet-Kiwi-XXL. Finally, as a separate track, we perform a small-scale humanevaluation of the plausibility of explanations given by the LLMs.

The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics