LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914952719106048 |
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| author | Yang, Yi Duan, Hanyu Liu, Jiaxin Tam, Kar Yan |
| author_facet | Yang, Yi Duan, Hanyu Liu, Jiaxin Tam, Kar Yan |
| contents | The increasing use of text as data in social science research necessitates the development of valid, consistent, reproducible, and efficient methods for generating text-based concept measures. This paper presents a novel method that leverages the internal hidden states of large language models (LLMs) to generate these concept measures. Specifically, the proposed method learns a concept vector that captures how the LLM internally represents the target concept, then estimates the concept value for text data by projecting the text's LLM hidden states onto the concept vector. Three replication studies demonstrate the method's effectiveness in producing highly valid, consistent, and reproducible text-based measures across various social science research contexts, highlighting its potential as a valuable tool for the research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research Yang, Yi Duan, Hanyu Liu, Jiaxin Tam, Kar Yan Computation and Language The increasing use of text as data in social science research necessitates the development of valid, consistent, reproducible, and efficient methods for generating text-based concept measures. This paper presents a novel method that leverages the internal hidden states of large language models (LLMs) to generate these concept measures. Specifically, the proposed method learns a concept vector that captures how the LLM internally represents the target concept, then estimates the concept value for text data by projecting the text's LLM hidden states onto the concept vector. Three replication studies demonstrate the method's effectiveness in producing highly valid, consistent, and reproducible text-based measures across various social science research contexts, highlighting its potential as a valuable tool for the research community. |
| title | LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.12722 |