{
  "annotation_id": "e25bef50-e7b9-42fa-b807-39084e9584a3",
  "date_created": "2026-02-17T05:53:20.274000Z",
  "date_modified": "2026-02-17T05:53:20.274000Z",
  "file_hash": "8cbf1e8baf8ea5e6470188a9b46907d08847081cb0f52cbe91026651e6fdbb7a",
  "private": false,
  "record": {
    "abstract": "Large Language Models (LLMs), despite their remarkable capabilities across NLP tasks, struggle with phonologically-grounded phenomena like rhyme detection and generation. This is even more evident in lower-resource languages such as Modern Greek. In this paper, we present a hybrid system that combines LLMs with deterministic phonological algorithms to achieve accurate rhyme identification/analysis and generation. Our approach implements a comprehensive taxonomy of Greek rhyme types, including Pure, Rich, Imperfect, Mosaic, and Identical Pre-rhyme Vowel (IDV) patterns, and employs an agentic generation pipeline with phonological verification. We evaluate multiple prompting strategies (zero-shot, few-shot, Chain-of-Thought, and RAG-augmented) across several LLMs including Claude 3.7 and 4.5, GPT-4o, Gemini 2.0 and open-weight models like Llama 3.1 8B and 70B and Mistral Large. Results reveal a significant \"Reasoning Gap\": while native-like models (Claude 3.7) perform intuitively (40\\% accuracy in identification), reasoning-heavy models (Claude 4.5) achieve state-of-the-art performance (54\\%) only when prompted with Chain-of-Thought. Most critically, pure LLM generation fails catastrophically (under 4\\% valid poems), while our hybrid verification loop restores performance to 73.1\\%. We release our system and a crucial, rigorously cleaned corpus of 40,000+ rhymes, derived from the Anemoskala and Interwar Poetry corpora, to support future research.",
    "arxiv_id": "2601.09631",
    "authors": [
      "Stergios Chatzikyriakidis"
    ],
    "categories": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "LLMs Got Rhythm? Hybrid Phonological Filtering for Greek Poetry Rhyme Detection and Generation",
    "url": "https://arxiv.org/abs/2601.09631",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "45efca6c-f52a-4fe9-b498-dc078b102011",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}