BYOC：基于共同撰写的类别描述进行个性化少样本分类
BYOC: Personalized Few-Shot Classification with Co-Authored Class Descriptions
AI总结：
本文提出BYOC方法，通过用户与大型语言模型交互共同撰写类别描述进行少样本文本分类，无需大量标注数据，实验表明其性能接近大训练集模型，且个性化分类器准确率达90%。
摘要：
文本分类是许多自然语言处理应用中一个被广泛研究且多功能的构建模块。然而，现有方法要么需要大规模标注语料库来训练模型，要么在以大型语言模型为基础时，需要精心设计提示词，并且使用能容纳许多示例的长上下文。因此，终端用户无法自行构建分类器。为解决这一问题，我们提出了一种使用大型语言模型进行少样本文本分类的新方法。我们不是提供少样本示例，而是用每个类别的显著特征描述来提示大型语言模型。这些描述由用户和大型语言模型交互式共同撰写：当用户标注每个少样本示例时，大型语言模型会提出相关问题，用户进行回答。示例、问题和答案被总结以形成分类提示。我们的实验表明，我们的方法能够产生高准确率的分类器，其性能达到使用显著更大数据集训练的模型的82%，而仅使用其训练集的1%。此外，在一项有30名参与者参与的研究中，我们展示了终端用户能够构建适合其特定需求的分类器。这些个性化分类器显示出平均90%的准确率，比最先进的方法高出15%。
英文摘要：
Text classification is a well-studied and versatile building block for many NLP applications. Yet, existing approaches require either large annotated corpora to train a model with or, when using large language models as a base, require carefully crafting the prompt as well as using a long context that can fit many examples. As a result, it is not possible for end-users to build classifiers for themselves. To address this issue, we propose a novel approach to few-shot text classification using an LLM. Rather than few-shot examples, the LLM is prompted with descriptions of the salient features of each class. These descriptions are coauthored by the user and the LLM interactively: while the user annotates each few-shot example, the LLM asks relevant questions that the user answers. Examples, questions, and answers are summarized to form the classification prompt. Our experiments show that our approach yields high accuracy classifiers, within 82% of the performance of models trained with significantly larger datasets while using only 1% of their training sets. Additionally, in a study with 30 participants, we show that end-users are able to build classifiers to suit their specific needs. The personalized classifiers show an average accuracy of 90%, which is 15% higher than the state-of-the-art approach.
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。
