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Large Language Models for Data Annotation and Synthesis: A Survey

Summary

Labeling or generating training data by hand is slow, expensive and often requires domain experts. This survey examines how LLMs such as GPT-4, Gemini and LLaMA-2 can automate that work. It is a literature review, so it reports no new experiments. The authors frame the setting with two roles: an annotator model A that produces labels or synthetic data, and a task learner L that is trained on them. L can be another large model or a smaller one such as BERT.

The review is organized around a three-part taxonomy:

  • Generation: what kinds of annotations LLMs can produce.
  • Assessment: how the quality of those outputs is evaluated and filtered.
  • Utilization: how the outputs are used during fine-tuning, alignment and inference.

It also covers ethical and technical concerns, including bias, hallucination and computational cost, and lists practical tools in an appendix. The scope is text-only LLMs, so multimodal systems like LLaVA are largely excluded. The survey's value is a structured map of a fast-moving area that other LLM surveys treat only briefly.

Key points

  • Annotation types covered: instruction & response pairs, labels, rationales, pairwise feedback, textual feedback and domain-specific data. Diversity and quality are treated as the central criteria for generated data.
  • Assessment and filtering methods: general and task-specific evaluation, plus filtering that is rule-based, relies on external sources, or is driven by LLMs.
  • Utilization stages:
    • supervised fine-tuning, through self-distillation or distillation into smaller models;
    • alignment tuning, through reward modeling and policy training;
    • inference time, through in-context learning and reasoning.
  • Notable resources reviewed: Persona Hub (about 1 billion web-derived personas), Magpie, FANNO, AttrPrompt, Source2Synth and CoAnnotating (human–LLM co-annotation).
  • Mitigations discussed: for hallucination, Reverse Validation and CoT-based explanations; for efficiency, pruning, Mixture of Experts and quantization.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

90 papers of our library cite it (of the 140884 whose bibliographies we read); 5 of them have a page here, 85 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.emnlp-main.54

@inproceedings{tan2024large,
  title = {{Large Language Models for Data Annotation and Synthesis: A Survey}},
  author = {Tan, Zhen and Li, Dawei and Wang, Song and Beigi, Alimohammad and Jiang, Bohan and Bhattacharjee, Amrita and Karami, Mansooreh and Li, Jundong and Cheng, Lu and Liu, Huan},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.54},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.54}
}

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