{
  "annotation_id": "a078f711-9128-4e7c-815c-f57f911371c3",
  "date_created": "2026-02-17T05:52:52.467000Z",
  "date_modified": "2026-02-17T05:52:52.467000Z",
  "file_hash": "5c452d8e294d792120804cc3f37a0f4031d7908a78c4c57260263dd8aa2544a6",
  "private": false,
  "record": {
    "abstract": "This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tuning and no explicit clues about the task. We evaluated our approach on cryptic column name expansion (CNE) in database tables, a task which is critical for tabular data search, access, and understanding and yet there has been very little existing work. We evaluated on datasets in two languages, English and German. This is the first work to report on the application of automatic prompt engineering for the CNE task. To the best of our knowledge, this is also the first work on the application of automatic prompt engineering for a language other than English.",
    "arxiv_id": "2601.03130",
    "authors": [
      "Faisal Chowdhury",
      "Nandana Mihindukulasooriya",
      "Niharika S D\u0027Souza",
      "Horst Samulowitz",
      "Neeru Gupta",
      "Tomasz Hanusiak",
      "Michal Kapitonow"
    ],
    "categories": [
      "cs.AI",
      "cs.CL"
    ],
    "journal_ref": "The IEEE International Conference on Data Mining (ICDM) 2025 : Demo Track",
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "title": "Automatic Prompt Engineering with No Task Cues and No Tuning",
    "url": "https://arxiv.org/abs/2601.03130",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "57893f2a-0b3c-4a80-9184-bec99b203568",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}