{
  "id": "edlm",
  "title": "Energy-Based Diffusion Language Models for Text Generation",
  "authors": [
    {
      "given": "Minkai",
      "family": "Xu"
    },
    {
      "given": "Tomas",
      "family": "Geffner"
    },
    {
      "given": "Karsten",
      "family": "Kreis"
    },
    {
      "given": "Weili",
      "family": "Nie"
    },
    {
      "given": "Yilun",
      "family": "Xu"
    },
    {
      "given": "Jure",
      "family": "Leskovec"
    },
    {
      "given": "Stefano",
      "family": "Ermon"
    },
    {
      "given": "Arash",
      "family": "Vahdat"
    }
  ],
  "year": 2025,
  "venue": "ICLR 2025",
  "paper_url": "https://arxiv.org/html/2410.21357v4",
  "summary": "在每一步扩散去噪分布中加入完整序列的残差能量，采用自回归模型或 NCE 参数化，并通过并行重要性采样生成文本。",
  "connection": "直接引用 Deng 等人（2020），将残差修正与配分函数估计用于扩散语言建模。",
  "bibtex_type": "inproceedings",
  "citation_key": "edlm",
  "source_urls": [
    "https://arxiv.org/html/2410.21357v4"
  ],
  "arxiv_id": "2410.21357",
  "arxiv_first_posted": "2024-10-28",
  "updated_at": "2026-11-11",
  "description_language": "zh-Hans",
  "abstract_language": "en",
  "record_url": "https://yuntiandeng.com/research/zh/papers/edlm/"
}