arXiv cs.CL· Noah Dasanaike·· 3 天前AI 评分24
预训练语言模型作为社会科学研究的零样本工具
Pre-Trained Language Models as Zero-Shot Tools for Social Science Research
AI 导读
预训练语言模型无需任务特定训练即可完成记录链接与姓名人口统计推断。作者提出的 EnsembleLink 结合预训练嵌入与字符串评分器，性能匹配或超越多数既有方法，并能泛化到后者无法处理的文字与格式。LLM 在美国选民文件上的族裔推断准确率达 83.8%，高于 BISG 的 78.0%，开放权重模型可复现且精度接近，还能推断宗教、种姓等美国以外群体类别并减少下游地理种族差距估计的偏差。
正文 · 原文
Abstract:Record linkage and demographic inference from names are routine demands in social science. Most approaches to these problems require labeled training data that are difficult or costly to acquire. I show that pre-trained language models perform both tasks accurately without requiring task-specific training. For record linkage, I introduce EnsembleLink, an algorithm that uses pre-trained embeddings alongside string-based scorers to retrieve a short list of candidates and then identify the likeliest matches. EnsembleLink matches or exceeds the performance of most prior approaches while generalizing across scripts and formats that those methods are unable to handle. For ethnicity inference from names, I show that large language models (LLMs) achieve up to 83.8% accuracy on U.S. voter files, compared to 78.0% for name-supplemented BISG. Open-weight models offer reproducible implementations that approach these models in accuracy. Moreover, LLMs are able to infer group membership for religious, caste-based, and other ethnic group categories outside of the United States, and reduce bias in downstream estimates of geographic racial gaps. Where pre-training encodes the world knowledge required by each task, task-specific labels add less information.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.19048 [cs.CL] |
| (or arXiv:2610.19048v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19048 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Noah Dasanaike [view email]
[v1]
Tue, 13 Oct 2026 18:43:04 UTC (266 KB)
来源：arXiv cs.CL · arxiv.org