{
  "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": "Adds a full-sequence residual energy to each diffusion denoising distribution, with autoregressive or NCE parameterizations and parallel importance sampling.",
  "connection": "Directly cites Deng et al. (2020), carrying residual correction and partition estimation into diffusion language modeling.",
  "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": "2026-11-10",
  "record_url": "https://yuntiandeng.com/research/papers/edlm/"
}