ChiEngMixBench: A New Frontier for Understanding Code-Mixing in Generative AI
Analysis
This research introduces ChiEngMixBench, a groundbreaking benchmark designed to evaluate how well Large Language Models (LLMs) handle the increasingly common practice of code-mixing in human-LLM interactions. It formulates code-mixing as a cognitive alignment problem, offering a novel perspective on how to assess the context-appropriateness of language models in multilingual scenarios.
Key Takeaways
- •ChiEngMixBench provides a benchmark for evaluating Large Language Model (LLM) performance in Chinese-English code-mixing scenarios.
- •The benchmark focuses on two key aspects of code-mixing: spontaneity and naturalness.
- •The study reveals an 'implicitly emergent Terminology Layering Strategy' aligned with Matrix Language Frame (MLF) theory.
Reference / Citation
View Original"Empirical evaluation shows that our metrics can systematically distinguish code-mixing performance across models."
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ArXiv NLPJan 26, 2026 05:00
* Cited for critical analysis under Article 32.