论文标题
气味描述符通过提示理解
Odor Descriptor Understanding through Prompting
论文作者
论文摘要
当代自然语言处理（NLP）模型的嵌入通常被用作单词或句子的数值表示。但是，诸如“皮革”或“果味”之类的气味描述符单词之间的常见用法和嗅觉使用量很大，因此传统的产生这些嵌入的方法不足。在本文中，我们提出了两种方法，以生成与现成的嵌入相比，与它们的嗅觉含义更加紧密相符的气味单词的嵌入。这些生成的嵌入量优于先前的最先进的和现代的微调/提示方法，这些方法是在特定于零气味的NLP基准上。
Embeddings from contemporary natural language processing (NLP) models are commonly used as numerical representations for words or sentences. However, odor descriptor words, like "leather" or "fruity", vary significantly between their commonplace usage and their olfactory usage, as a result traditional methods for generating these embeddings do not suffice. In this paper, we present two methods to generate embeddings for odor words that are more closely aligned with their olfactory meanings when compared to off-the-shelf embeddings. These generated embeddings outperform the previous state-of-the-art and contemporary fine-tuning/prompting methods on a pre-existing zero-shot odor-specific NLP benchmark.
