@inproceedings{panahi-etal-2026-llms,
    title = "When {LLM}s Annotate: Reliability Challenges in Low-Resource {NLI}",
    author = "Panahi, Solmaz  and
      Kelleher, John  and
      Nedumpozhimana, Vasudevan",
    editor = "Hettiarachchi, Hansi  and
      Ranasinghe, Tharindu  and
      Plum, Alistair  and
      Rayson, Paul  and
      Mitkov, Ruslan  and
      Gaber, Mohamed  and
      Premasiri, Damith  and
      Tan, Fiona Anting  and
      Uyangodage, Lasitha",
    booktitle = "Proceedings of the Second Workshop on Language Models for Low-Resource Languages ({L}o{R}es{LM} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.loreslm-1.17/",
    pages = "178--188",
    ISBN = "979-8-89176-377-7",
    abstract = "This paper systematically evaluates LLM reliability on the complex semantic task of Natural Language Inference (NLI) in Farsi, assessing six prominent models across eight prompt variations through a multi-dimensional framework that measures accuracy, prompt sensitivity, and intra-class consistency. Our results demonstrate that prompt design{---}particularly the order of premise and hypothesis{---}significantly impacts prediction stability. Proprietary models (Claude-Opus-4, GPT-4o) exhibit superior stability and accuracy compared to open-weight alternatives. Across all models, the `Neutral' class emerges as the most challenging and least stable category. Crucially, we redefine model instability as a diagnostic tool for benchmark quality, demonstrating that observed disagreement often reflects valid challenges to ambiguous or erroneous gold-standard labels."
}