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home / papers / 2026 / 大模型参数高效微调方法综述: 技术、趋势与挑战

大模型参数高效微调方法综述: 技术、趋势与挑战

Summary

Adapting large pre-trained models to specific tasks by updating every weight is costly in compute and memory. It can also erode general abilities through catastrophic forgetting. This Chinese-language survey from Peking University reviews parameter-efficient fine-tuning (PEFT), which trains only a small set of parameters and leaves the backbone largely frozen. The authors offer a unifying view: PEFT methods move optimization out of the full high-dimensional weight space and into a compact, constrained subspace that still has strong representational power. They sort existing work into four families: additive, selective, reparameterized and hybrid. Each family is analyzed for its mechanism, performance profile, typical use cases and strengths. The paper traces how the field shifted from isolated new techniques toward balancing storage, computation and performance. It predicts a move toward unified frameworks built on automation and hardware-software co-design. Representative methods are compared quantitatively on a shared model and datasets. The survey also covers uses in vision, speech, multimodal and federated settings. For teams with limited GPUs, such as those adapting LLMs to Ukrainian, it serves as a practical map of the available options.

Key points

  • Four-way taxonomy:
    • Additive methods add new trainable parts, either adapters or soft prompts.
    • Selective methods tune a subset of the existing weights.
    • Reparameterized methods train a low-dimensional reparameterization during training and convert it back to the original form for inference.
    • Hybrid methods combine several of these strategies.
  • Methods are compared on two criteria: whether trained parameters can be merged back into the base weights, and how much inference latency they add.
  • A standard adapter has three parts: a down-projection, a nonlinearity and an up-projection, joined by a residual connection. Adapters can be inserted serially or in parallel.
  • Evaluation benchmarks:
    • GLUE, with nine tasks: CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE and WNLI.
    • Commonsense benchmarks: OpenBookQA, PIQA, SocialIQA, HellaSwag, BoolQ, WinoGrande (about 44,000 questions) and ARC (easy and challenge splits).
  • Publication details: Acta Automatica Sinica, 2026, vol. 52, no. 8, pp. 1520–1556, DOI 10.16383/j.aas.c250451.

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: Linguistics & languages · Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.16383/j.aas.c250451

@inproceedings{2026,
  title = {{大模型参数高效微调方法综述: 技术、趋势与挑战}},
  author = {唐岸达 and 林宙辰},
  booktitle = {自动化学报 (Acta Automatica Sinica)},
  year = {2026},
  doi = {10.16383/j.aas.c250451},
  url = {https://doi.org/10.16383/j.aas.c250451}
}

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