{
  "id": 10982,
  "url": "https://arxiv.org/abs/2607.14605",
  "title": "Investigating first-language bias in LLM-based automated essay scoring: A cross-prompt evaluation of an open-weight AI-model on TOEFL essays",
  "summary": "arXiv:2607.14605v1 Announce Type: cross Abstract: This study examines the cross-prompt generalization and first-language (L1) scoring effects of a LoRA-adapted open-weight large language model (Gemma-3-27B-it) applied to automated essay scoring. Using the identical model and inference configuration reported in \"AiAWE: An Open-Source LLM Automated Writing Evaluation System Using LoRA-Adapted Instruction-Tuned Models\" (Gayed, 2026), which was fine-tuned on 480 argumentative essays from two prompts",
  "authors": "John Maurice Gayed",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-17T04:00:00.000Z",
  "fetched_at": "2026-07-17T05:10:53.887Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/10982",
  "original_url": "https://arxiv.org/abs/2607.14605",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}