ValueByte-AI/Awesome-LLM-in-Social-Science

• highreadme#1

  Reposition README H1 and opening paragraph to clarify 'awesome list' identity

  原因：

  当前

  # Awesome-LLM-in-Social-Science
  
  > **🔗 Recommended Resource:**  
  > Check out Awesome-LLM-Psychometrics for a comprehensive collection of papers and resources on LLM psychometrics, including evaluation, validation, and enhancement.  
  >  
  
  Below we compile *awesome* papers that  
  evaluate** Large Language Models (LLMs) from a perspective of Social Science.

  复制粘贴的修复

  复制

  # Awesome-LLM-in-Social-Science: A Curated List of Papers on LLMs in Social Science
  
  This repository is a curated collection of *awesome* papers that explore the intersection of Large Language Models (LLMs) and Social Science. We compile research that evaluates and aligns LLMs from a Social Science perspective, employs LLMs to facilitate research, and contributes surveys, perspectives, and datasets on these topics.

  待办完成忽略
• mediumhomepage#2

  Add a homepage URL to the repository's About section

  原因：

  复制粘贴的修复

  复制

  https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science

  待办完成忽略
• lowreadme#3

  Reorder README introduction to prioritize this repo's identity

  原因：

  当前

  # Awesome-LLM-in-Social-Science
  
  > **🔗 Recommended Resource:**  
  > Check out Awesome-LLM-Psychometrics for a comprehensive collection of papers and resources on LLM psychometrics, including evaluation, validation, and enhancement.  
  >  
  
  Below we compile *awesome* papers that  
  evaluate** Large Language Models (LLMs) from a perspective of Social Science.

  复制粘贴的修复

  复制

  # Awesome-LLM-in-Social-Science: A Curated List of Papers on LLMs in Social Science
  
  This repository is a curated collection of *awesome* papers that explore the intersection of Large Language Models (LLMs) and Social Science. We compile research that evaluates and aligns LLMs from a Social Science perspective, employs LLMs to facilitate research, and contributes surveys, perspectives, and datasets on these topics.
  
  > **🔗 Recommended Resource:**  
  > Check out Awesome-LLM-Psychometrics for a comprehensive collection of papers and resources on LLM psychometrics, including evaluation, validation, and enhancement.

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to evaluate large language models using social science methodologies?

  你：未被推荐
  AI 推荐顺序：

  1. Appen
  2. Scale AI
  3. Amazon Mechanical Turk
  4. Qualtrics
  5. SurveyMonkey
  6. Google Forms
  7. NVivo
  8. ATLAS.ti
  9. Dedoose
  10. PsychoPy
  11. OpenSesame
  12. R
  13. Python
  14. SciPy
  15. StatsModels
  16. Pandas
  17. SPSS
  18. Stata
  19. NetLogo
  20. Mesa

  AI 推荐了 20 个替代方案，却始终没点名 ValueByte-AI/Awesome-LLM-in-Social-Science。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Where to find research on LLM applications in social psychology and economic modeling?

  你：未被推荐
  AI 推荐顺序：

  1. Google Scholar
  2. arXiv
  3. SSRN
  4. JSTOR
  5. ACM Digital Library
  6. IEEE Xplore
  7. Nature Human Behaviour
  8. Science
  9. PNAS
  10. Journal of Economic Perspectives
  11. Psychological Science

  AI 推荐了 11 个替代方案，却始终没点名 ValueByte-AI/Awesome-LLM-in-Social-Science。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of ValueByte-AI/Awesome-LLM-in-Social-Science?

  pass
  ⚠AI 未点名 ValueByte-AI/Awesome-LLM-in-Social-Science —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts ValueByte-AI/Awesome-LLM-in-Social-Science in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 ValueByte-AI/Awesome-LLM-in-Social-Science

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo ValueByte-AI/Awesome-LLM-in-Social-Science solve, and who is the primary audience?

  pass
  ⚠AI 未点名 ValueByte-AI/Awesome-LLM-in-Social-Science —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？