{
  "annotation_id": "a162c7b1-2d87-4af4-9216-52f090dc9691",
  "date_created": "2026-02-17T05:53:19.292000Z",
  "date_modified": "2026-02-17T05:53:19.292000Z",
  "file_hash": "7e217f56326dfb7a83d1447ef067e3ca3d147f54d7d6afaa0b5f62d594213a3a",
  "private": false,
  "record": {
    "abstract": "The rise of online counseling services has highlighted the need for effective training methods for future counselors. This paper extends research on VirCo, a Virtual Client for Online Counseling, designed to complement traditional role-playing methods in academic training by simulating realistic client interactions. Building on previous work, we introduce a new dataset incorporating adversarial attacks to test the ability of large language models (LLMs) to maintain their assigned roles (role-consistency). The study focuses on evaluating the role consistency and coherence of the Vicuna model\u0027s responses, comparing these findings with earlier research. Additionally, we assess and compare various open-source LLMs for their performance in sustaining role consistency during virtual client interactions. Our contributions include creating an adversarial dataset, evaluating conversation coherence and persona consistency, and providing a comparative analysis of different LLMs.",
    "arxiv_id": "2601.08892",
    "authors": [
      "Eric Rudolph",
      "Natalie Engert",
      "Jens Albrecht"
    ],
    "categories": [
      "cs.CL",
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Evaluating Role-Consistency in LLMs for Counselor Training",
    "url": "https://arxiv.org/abs/2601.08892",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c17bf736-cf6b-4d08-b031-000f9ce0bc22",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}