This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Language Models to handle the task of quality estimation, particularly in the context of evaluating machine translations and summaries. We conducted systematic experiments with various prompting techniques, including standard prompting, prompts informed by annotator instructions, and innovative chain-of-thought prompting. In addition, we integrated these approaches with zero-shot and one-shot learning methods to maximize the efficacy of our evaluation procedures. Our work reveals that combining these approaches using a "small", open source model (orca_mini_v3_7B) yields competitive results.

翻译：本文描述并分析了我们参与2023年Eval4NLP共享任务的情况，该任务旨在评估基于提示技术赋能大型语言模型处理质量估计任务的有效性，特别是在机器翻译与摘要评估的语境中。我们系统性地开展了多种提示技术的实验，包括标准提示、基于标注者指令的提示，以及创新的思维链提示。此外，我们将这些方法与零样本和单样本学习方法相结合，以最大化评估流程的效能。研究结果表明，使用"小型"开源模型（orca_mini_v3_7B）整合上述方法可取得具有竞争力的结果。