Large language models have seen extraordinary growth in popularity due to
their human-like content generation capabilities. We show that these models can
also be used to successfully cluster human-generated content, with success
defined through the measures of distinctiveness and interpretability. This
success is validated by both human reviewers and ChatGPT, providing an
automated means to close the 'validation gap' that has challenged short-text
clustering. Comparing the machine and human approaches we identify the biases
inherent in each, and question the reliance on human-coding as the 'gold
standard'. We apply our methodology to Twitter bios and find characteristic
ways humans describe themselves, agreeing well with prior specialist work, but
with interesting differences characteristic of the medium used to express
identity.

大语言模型在内容生成能力方面取得了巨大的增长，本研究展示了这些模型还可以成功地对人类生成的内容进行聚类，通过独特性和可解释性两个度量标准来定义成功。该研究验证了这一成功，通过人工审阅和 ChatGPT 的比较，提供了一种自动化的方法来弥合挑战短文本聚类的 “验证差距”。比较人工和机器方法，我们确定了每种方法固有的偏见，并质疑依赖人工编码作为 “黄金标准”。我们将该方法应用于 Twitter 个人资料描述信息，并发现人类自我描述的特点方式，与先前专家工作相吻合，但也有表达身份特点的有趣差异，这与表达身份的媒介特性有关。

基于大型语言模型的短文本人可解释聚类

Human-interpretable clustering of short-text using large language models

Text Style Transfer (TST) evaluation is, in practice, inconsistent.
Therefore, we conduct a meta-analysis on human and automated TST evaluation and
experimentation that thoroughly examines existing literature in the field. The
meta-analysis reveals a substantial standardization gap in human and automated
evaluation. In addition, we also find a validation gap: only few automated
metrics have been validated using human experiments. To this end, we thoroughly
scrutinize both the standardization and validation gap and reveal the resulting
pitfalls. This work also paves the way to close the standardization and
validation gap in TST evaluation by calling out requirements to be met by
future research.

进行元分析发现 TST 评估在人工和自动化评估方面存在实践上的不一致和显著的标准化和验证差距，提出了要求未来研究满足的需求，以弥补这些差距。