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Zhen-Tan-dmml / LLM4Annotation

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Large Language Models for Data Annotation: A Survey

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[Large Language Models for Data Annotation: A Survey]

If you find this repo helpful, we would appreciate it if you could cite our survey.

@misc{tan2024large,
      title={Large Language Models for Data Annotation: A Survey}, 
      author={Zhen Tan and Alimohammad Beigi and Song Wang and Ruocheng Guo and Amrita Bhattacharjee and Bohan Jiang and Mansooreh Karami and Jundong Li and Lu Cheng and Huan Liu},
      year={2024},
      eprint={2402.13446},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

LLM-Based Data Annotation

Manually Engineered Prompt

Alignment via Pairwise Feedback

Assessing LLM-Generated Annotations

Evaluating LLM-Generated Annotations

Data Selection via Active Learning

Learning with LLM-Generated Annotations

Target Domain Inference: Direct Utilization of Annotations

Knowledge Distillation: Bridging LLM and task-specific models

Harnessing LLM Annotations for Fine-Tuning and Prompting

In-Context Learning (ICL)

Chain-of-Thought Prompting (CoT)

Instruction Tuning (IT)

Alignment Tuning (AT)

Surveys

Toolkits