{
  "annotation_id": "6b347e32-24b6-495f-9bf2-840faa36c44c",
  "date_created": "2026-02-17T05:53:05.004000Z",
  "date_modified": "2026-02-17T05:53:05.004000Z",
  "file_hash": "526be3fd135a546d66e3dd9049569ab5bdd7186ce2ea9ea2edc31b927e9e1841",
  "private": false,
  "record": {
    "abstract": "Meta-learning methods for regression like Neural (Diffusion) Processes achieve impressive results, but with these models it can be difficult to incorporate expert prior knowledge and information contained in metadata. Large Language Models (LLMs) are trained on giant corpora including varied real-world regression datasets alongside their descriptions and metadata, leading to impressive performance on a range of downstream tasks. Recent work has extended this to regression tasks and is able to leverage such prior knowledge and metadata, achieving surprisingly good performance, but this still rarely matches dedicated meta-learning methods. Here we introduce a general method for sampling from a product-of-experts of a diffusion or flow matching model and an `expert\u0027 with binned probability density; we apply this to combine neural diffusion processes with LLM token probabilities for regression (which may incorporate textual knowledge), exceeding the empirical performance of either alone.",
    "arxiv_id": "2601.06147",
    "authors": [
      "Felix Biggs",
      "Samuel Willis"
    ],
    "categories": [
      "cs.LG",
      "cs.CL",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "LLM Flow Processes for Text-Conditioned Regression",
    "url": "https://arxiv.org/abs/2601.06147",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d5e018b1-e6d3-49f1-8f35-26be0c60bb86",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}