Introduction: A Simple Question That Is Hard to Answer

“Which model has the best Chinese-language ability?” is a question frequently debated in developer communities. However, experienced engineers quickly realize that the question lacks a clear metric. One developer may find a model impressive when writing fiction; another may prefer a different model for contract drafting; and yet another may reach opposite conclusions when testing dialects, classical poetry, or internet slang. Claims that a model is “far ahead” or “has gotten worse” are often based on personal impressions from a narrow set of use cases. Such judgments have small samples, limited scenarios, and usually cannot support broad conclusions.

A more useful engineering question is therefore: How can we build a scientific, reproducible, and transferable system for evaluating Chinese-language expression ability? This article does not rank specific products. Instead, it focuses on evaluation methodology itself—the capability teams need when developing their own models, procuring capabilities, or making A/B selection decisions.

1. Why Subjective Impressions Are Not Enough

Subjective evaluation has three structural weaknesses:

1. Survivorship bias and limited scenario coverage: A single conversation covers only one slice of model capability. Fluent writing does not imply rigorous logic; the ability to write poetry does not imply the ability to write clear technical documentation.
2. Lack of reproducibility: Model outputs are affected by random sampling, prompt wording, system settings, and other factors. The same model can perform very differently under different prompts, so a single experience cannot reflect the true distribution.
3. Evaluation drift: The evaluator’s language preferences change with the task. “Beautiful prose” and “accurate information” often conflict, making it difficult to measure them with one standard.

The solution is to break the vague notion of “good” into sub-dimensions that can be defined, scored, and aggregated.

2. A Multi-Dimensional Chinese-Language Capability Framework

A reasonably complete evaluation of Chinese-language expression can be broken down into at least the following six dimensions:

• Semantic understanding and reasoning: Whether the model accurately understands instruction intent, implicit assumptions, and logical relationships; how it handles coreference, ambiguity, and multi-turn context.
• Fluency and normativity: Grammatical correctness, word choice, punctuation conventions, switching between written and spoken styles, and whether the output contains translationese or grammatical errors.
• Cultural context and localized expression: Understanding and appropriate use of idioms, colloquialisms, poetry, allusions, festivals, customs, and internet slang, while avoiding cultural misuse.
• Genre coverage and style control: Whether the model can switch between genres such as official documents, fiction, poetry, expository writing, and marketing copy, and whether it can follow requirements for length, tone, and format.
• Long-context coherence: Whether the model maintains topic consistency, clear structure, and no contradictions in long-form writing or long-document processing, and whether it forgets earlier context.
• Factual consistency and hallucination resistance: Reducing factual errors in Chinese knowledge question answering, being cautious with uncertain information, and not fabricating names, places, data, or citations.

These dimensions usually need to be scored separately and then weighted and aggregated, rather than reduced to one general score.

3. Building a Reproducible Evaluation Set

The credibility of an evaluation depends on the coverage and representativeness of the test set. The following principles are recommended:

1. Scenario stratification: Divide test sets by use case, such as creative writing, technical documentation, knowledge question answering, translation and rewriting, dialogue interaction, and structured extraction. Each scenario should have enough samples.
2. Difficulty stratification: Distinguish basic capability, such as simple instruction following, from advanced capability, such as complex constraints, long-chain reasoning, and implicit intent.
3. Adversarial samples: Include ambiguous sentences, inducements that should be refused, and obscure knowledge that is likely to cause hallucinations, in order to test robustness.
4. Version management: Put the evaluation set under version control and expand it as models evolve to avoid overfitting to a fixed test set.

A schematic evaluation set structure in JSON can be organized as follows:

{
  "scene": "creative_writing",
  "id": "cw_001",
  "prompt": "Write a prose passage of at least 200 words titled 'A Rainy Night'. Do not use metaphors.",
  "rubric": {
    "fluency": 0.3,
    "style_control": 0.4,
    "coherence": 0.3
  },
  "reference_notes": "Focus on whether the model follows the constraint of not using metaphors."
}

4. Automated Evaluation Methods

Manual evaluation is costly and slow. In practice, a combined strategy of “automation first, with human calibration as a supplement” is usually used.

4.1 Rule-Based and Metric-Based Evaluation

For tasks that can be judged automatically—whether constraints are followed, whether the format is correct, whether the length meets requirements, or whether forbidden words appear—rule-based checks can be used directly. These metrics are objective and have zero cost:

def check_constraints(text: str, rubric: dict) -> dict:
    result = {}
    # Length constraint
    if "min_len" in rubric:
        result["length_ok"] = len(text) >= rubric["min_len"]
    # Forbidden-word constraint
    if "forbidden_words" in rubric:
        result["no_forbidden"] = not any(
            w in text for w in rubric["forbidden_words"]
        )
    # Paragraph-count constraint
    if "paragraphs" in rubric:
        paras = [p for p in text.split("\n\n") if p.strip()]
        result["paragraph_ok"] = len(paras) == rubric["paragraphs"]
    return result

4.2 LLM-as-a-Judge Evaluation

For dimensions that are difficult to quantify with rules, such as fluency and cultural appropriateness, an evaluation model can be introduced to score outputs according to predefined criteria. The key is judge prompt design and scoring consistency validation:

JUDGE_PROMPT = """
You are a strict reviewer of Chinese language quality. Evaluate the {dimension} performance of the following text on a scale of 1 to 5.
Output JSON only, in the format {"score": <int>, "reason": "<brief explanation>"}
Evaluation dimension: {dimension}
Text to evaluate:
{text}
"""
def evaluate_dimension(text: str, dimension: str, judge_call) -> dict:
    prompt = JUDGE_PROMPT.format(dimension=dimension, text=text)
    raw = judge_call(prompt)
    return parse_json(raw)

When using this approach, it is important to remember that the judge model itself has preferences. You should rotate positions (randomize A/B order), take averages across multiple samples, and regularly calibrate the judge model’s scoring bias against human annotations.

5. Human Evaluation and Scoring Design

Automation cannot fully replace human judgment, especially for aesthetic and cultural aspects. The key to human evaluation is reducing subjective noise:

• Clear scoring rubrics: Provide concrete anchor descriptions for each dimension on a 1–5 scale, avoiding “feel-based” scoring.
• Blind evaluation: Hide model identities and randomly shuffle output order to reduce bias caused by brand expectations.
• Multiple cross-scorers: Use at least 2–3 reviewers, calculate inter-rater agreement such as the Kappa coefficient, and separately review samples with large disagreement.
• Pairwise comparison: Asking reviewers “which one is better” is often more stable than direct scoring, especially when comparing two candidates with similar capabilities.

6. Common Pitfalls

1. Testing only strengths, not weaknesses: Impressive performance on one type of task does not mean overall usability. Weakness scenarios must be covered.
2. Prompts that favor a particular model: Different models are sensitive to prompt styles in different ways. Inconsistent prompts can create systematic bias, so prompt normalization should be performed.
3. Ignoring sampling randomness: A single generation can fluctuate greatly. Multiple samples should be collected and their distribution analyzed, rather than drawing conclusions from one example.
4. Test-set leakage: Repeatedly using the same public test sets can lead to “score hacking.” Rotate or expand held-out sets periodically.
5. Overreliance on a single judge model: This introduces the judge model’s own bias and should be cross-validated.

7. Applying the Framework to Engineering Model Selection

In real model-selection or capability-integration scenarios, the following workflow is recommended:

1. Identify the business scenario and select the 2–3 core metrics most relevant to the business from the six dimensions.
2. Build an evaluation set that closely matches real business use, ensuring sufficient sample size and representativeness.
3. First run rule-based automated evaluation, then batch-score with judge models, and finally calibrate with human spot checks.
4. Obtain comparable quantitative results through weighted aggregation, and record confidence intervals.
5. After deployment, continuously sample real traffic for regression testing and monitor how capability changes across model versions.

If a team already uses large language model services on a cloud platform, the evaluation workflow can be combined with common cloud capabilities such as logging, monitoring and alerting, and object storage: use logging to retain request and output samples, use object storage to archive evaluation sets and historical results, and use monitoring and alerting to track fluctuations in key metrics. Platforms such as Baidu AI Cloud typically provide infrastructure covering model services, data storage, and monitoring, which can help make the evaluation system engineering-grade and routine.

Conclusion

There is no permanent answer to “which model has the strongest Chinese ability,” because Chinese-language capability is multi-dimensional, scenario-dependent, and constantly evolving. The professional approach is to turn the question into: “In a specific business scenario, how can we objectively measure Chinese-language expression ability?” Building a dimensional, reproducible evaluation system that combines automation and human review not only helps you make rational selection decisions, but also forms a foundational engineering capability for continuously optimizing in-house models and ensuring quality. Rather than chasing vague slogans about being “far ahead,” let data speak.