{
  "annotation_id": "f145d0f1-9b22-4c97-b644-e6231a9a3187",
  "date_created": "2026-02-17T05:53:11.949000Z",
  "date_modified": "2026-02-17T05:53:11.949000Z",
  "file_hash": "d8c6852551ed8388815c59f427415452f1266e81f6f07b92c52d8ab4cdd386dc",
  "private": false,
  "record": {
    "abstract": "In human conversation, both interlocutors play an active role in maintaining mutual understanding. When addressees are uncertain about what speakers mean, for example, they can request clarification. It is an open question for language models whether they can assume a similar addressee role, recognizing and expressing their own uncertainty through clarification. We argue that reference games are a good testbed to approach this question as they are controlled, self-contained, and make clarification needs explicit and measurable. To test this, we evaluate three vision-language models comparing a baseline reference resolution task to an experiment where the models are instructed to request clarification when uncertain. The results suggest that even in such simple tasks, models often struggle to recognize internal uncertainty and translate it into adequate clarification behavior. This demonstrates the value of reference games as testbeds for interaction qualities of (vision and) language models.",
    "arxiv_id": "2601.07820",
    "authors": [
      "Manar Ali",
      "Judith Sieker",
      "Sina Zarrie\u00df",
      "Hendrik Buschmeier"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Reference Games as a Testbed for the Alignment of Model Uncertainty and Clarification Requests",
    "url": "https://arxiv.org/abs/2601.07820",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c13d8e86-748b-442e-bc6e-11159594122b",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}