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CNeo-Bench: Diagnosing Large Language Models on Chinese Neologisms

arXiv 2608.28053 ↗First posted 28 Aug 2026cs.CLcited by 0

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Abstract (arXiv)

Chinese neologisms exploit diverse and unique linguistic mechanisms, such as phonetic substitution (e.g., 886 for ``bye-bye'') and visual character decomposition that are rare in other languages. We introduce CNeo-Bench, a benchmark of 4,759 such neologisms with reference definitions, organized into five top-level categories and nine subcategories by the linguistic mechanism behind each expression. CNeo-Bench is paired with a two-tier evaluation framework that separates whether a model can describe a neologism from whether it can operate on its underlying mechanism. Evaluating 18 LLMs, we find that Chinese neologisms remain an open challenge; most models fall below 40% on definition generation, and on several subcategories a systematic recognition-manipulation gap emerges: models describe neologisms correctly but, in source-form restoration tasks, substitute a semantic equivalent (paraphrase) for the source form rather than producing the source form itself. A few-shot analysis on 1,058 hard items shows that in-context examples can solve many difficult cases, but leave a noticeable portion of errors remaining, indicating challenges beyond prompting alone can address.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 28 Aug 2026.

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References · 7

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2025Qwen3 Technical Reportcited by 8,195
  3. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
  4. 2024LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Tracescited by 28
  5. 2024NEO-BENCH: Evaluating Robustness of Large Language Models with Neologismscited by 19
  6. 2026NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learningcited by 3
  7. 2026Benchmarking Machine Translation on Chinese Social Media Textscited by 2

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