{
  "id": "tree-prompting",
  "title": "Tree Prompting: Efficient Task Adaptation without Fine-Tuning",
  "authors": [
    {
      "given": "John X.",
      "family": "Morris"
    },
    {
      "given": "Chandan",
      "family": "Singh"
    },
    {
      "given": "Alexander M.",
      "family": "Rush"
    },
    {
      "given": "Jianfeng",
      "family": "Gao"
    },
    {
      "given": "Yuntian",
      "family": "Deng"
    }
  ],
  "year": 2023,
  "venue": "EMNLP 2023",
  "paper_url": "https://arxiv.org/html/2310.14034v1",
  "summary": "学习可重复使用的决策树，其内部判断由接收提示词的语言模型调用完成；候选提示包括少样本示例、指令和动态生成的提示。",
  "connection": "基于提示的决策树原始工作：通过修改树结构和提示词适配任务，同时保持语言模型参数不变。",
  "bibtex_type": "inproceedings",
  "citation_key": "treeprompting",
  "source_urls": [
    "https://arxiv.org/html/2310.14034v1"
  ],
  "pdf_url": "https://aclanthology.org/2023.emnlp-main.384.pdf",
  "code_url": "https://github.com/csinva/tree-prompt",
  "doi": "10.18653/v1/2023.emnlp-main.384",
  "equal_contribution": [
    "John X. Morris",
    "Chandan Singh"
  ],
  "arxiv_id": "2310.14034",
  "arxiv_first_posted": "2023-10-21",
  "abstract": "Prompting language models (LMs) is the main interface for applying them to new tasks. However, for smaller LMs, prompting provides low accuracy compared to gradient-based fine-tuning. Tree Prompting is an approach to prompting which builds a decision tree of prompts, linking multiple LM calls together to solve a task. At inference time, each call to the LM is determined by efficiently routing the outcome of the previous call using the tree. Experiments on classification datasets show that Tree Prompting improves accuracy over competing methods and is competitive with fine-tuning. We also show that variants of Tree Prompting allow inspection of a model’s decision-making process.",
  "abstract_source": "https://arxiv.org/html/2310.14034v1",
  "updated_at": "2026-11-11",
  "description_language": "zh-Hans",
  "abstract_language": "en",
  "record_url": "https://yuntiandeng.com/research/zh/papers/tree-prompting/"
}