MiQA：隐喻问题推理的基准
MiQA: A Benchmark for Inference on Metaphorical Questions
摘要：
我们提出了一个基准来评估大语言模型与传统隐喻推理的能力。我们的基准测试将先前孤立的隐喻检测和常识推理主题结合到一个单一的任务中，该任务需要一个模型通过在字面和隐喻寄存器之间进行准确选择来进行推理。我们研究了最先进的预训练模型在二元选择任务上的表现，发现小型和超大型模型的表现存在很大差异，从偶然到接近人类水平。我们还分析了生成环境中最大的模型，发现虽然接近人类的表现，但需要仔细的多次提示。
英文摘要：
We propose a benchmark to assess the capability of large language models to reason with conventional metaphors. Our benchmark combines the previously isolated topics of metaphor detection and commonsense reasoning into a single task that requires a model to make inferences by accurately selecting between the literal and metaphorical register. We examine the performance of state-of-the-art pre-trained models on binary-choice tasks and find a large discrepancy between the performance of small and very large models, going from chance to near-human level. We also analyse the largest model in a generative setting and find that although human performance is approached, careful multiple-shot prompting is required.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
