CNeo-Bench: Diagnosing Large Language Models on Chinese Neologisms
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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.
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References · 7
- 2024The Llama 3 Herd of Modelscited by 14,192
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- 2024LLMSense: Harnessing LLMs for High-level Reasoning Over Spatiotemporal Sensor Tracescited by 28
- 2024NEO-BENCH: Evaluating Robustness of Large Language Models with Neologismscited by 19
- 2026NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learningcited by 3
- 2026Benchmarking Machine Translation on Chinese Social Media Textscited by 2
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