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This paper describes and analyzes our participation in the 2023 Eval4NLPshared task, which focuses on assessing the effectiveness of prompt-basedtechniques to empower Large Language Models to handle the task of qualityestimation, particularly in the context of evaluating machine translations andsummaries. We conducted systematic experiments with various promptingtechniques, including standard prompting, prompts informed by annotatorinstructions, and innovative chain-of-thought prompting. In addition, weintegrated these approaches with zero-shot and one-shot learning methods tomaximize the efficacy of our evaluation procedures. Our work reveals thatcombining these approaches using a "small", open source model (orca_mini_v3_7B)yields competitive results.

Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task