下一步回复预测X数据集：在粗心生成内容中的语言差异
Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content
AI总结：
本文提出通过X数据集评估LLM生成内容与人类生成内容的语言差异，旨在提升计算社会科学研究的准确性。
摘要：
随着大型语言模型（LLMs）作为人类参与者代理在社会科学研究中的日益使用，这代表了一个有前途但方法上具有风险的范式转变。尽管LLMs提供了可扩展性和成本效益，但其
英文摘要：
The increasing use of Large Language Models (LLMs) as proxies for human participants in social science research presents a promising, yet methodologically risky, paradigm shift. While LLMs offer scalability and cost-efficiency, their "naive" application, where they are prompted to generate content without explicit behavioral constraints, introduces significant linguistic discrepancies that challenge the validity of research findings. This paper addresses these limitations by introducing a novel, history-conditioned reply prediction task on authentic X (formerly Twitter) data, to create a dataset designed to evaluate the linguistic output of LLMs against human-generated content. We analyze these discrepancies using stylistic and content-based metrics, providing a quantitative framework for researchers to assess the quality and authenticity of synthetic data. Our findings highlight the need for more sophisticated prompting techniques and specialized datasets to ensure that LLM-generated content accurately reflects the complex linguistic patterns of human communication, thereby improving the validity of computational social science studies.
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。
