Evaluating and Engineering Better Chinese Expression in Large Language Models

Chinese expression quality is not simply a matter of whether a model can produce Chinese characters. For developers and enterprise users, it determines whether generated content can be used directly in business workflows: whether explanations are accurate, structure is clear, tone matches the context, facts are defensible, and long-form content remains consistent.

Claims such as “one model is far ahead in Chinese, while all others have declined” are often based on a small number of subjective examples. Without task definitions, scoring criteria, and version comparisons, these claims are neither reproducible nor useful for model selection. A more reliable approach is to treat Chinese expression as an engineering capability that can be measured, regressed, and optimized.

Decompose the Capability Instead of Ranking Models

Chinese expression includes at least five layers. Scoring them as one undifferentiated capability can hide the actual source of failure.

Semantic faithfulness: Does the response answer the question? Does it go off topic, omit conditions, or introduce key assumptions that were not provided? Constraints in the instruction—such as word limits, target audience, prohibited wording, and required points—should be checked individually.

Document organization: Do the title, paragraphs, and lists support comprehension, or do they merely accumulate generic language? A strong Chinese technical document typically states the conclusion first, followed by conditions, steps, boundaries, and exceptions. It avoids an empty “introduction–body–conclusion” structure that adds little information.

Natural language quality: Are word choices appropriate for formal technical writing? Does the text contain translationese, repetitive filler, incorrect collocations, or inconsistent punctuation? Natural language does not mean ornate language. Technical writing depends more on stable terminology and sentences that are easy to parse.

Style and role control: The same facts should be presented differently to developers, business leaders, and end users. Differences in detail and tone are necessary. If a model can only produce one generic “customer service” voice, its controllability is limited.

Factual and logical consistency: Numbers, causal relationships, timelines, and terminology definitions must not contradict one another. Polished writing with incorrect premises is more dangerous in enterprise settings than awkward wording.

Scoring these five dimensions separately helps identify whether the actual problem is poor writing, failure to follow constraints, or fluent but unreliable output.

Replace Impressions with a Task Set

Build a small but stratified evaluation set based on real traffic instead of comparing a few pieces of prose.

• Instruction following: Use rewriting tasks with multiple constraints, such as a word limit, a required risk statement, or a prohibition on claiming unverified capabilities.
• Information restructuring: Provide structured points and ask the model to produce an explanation, announcement, or troubleshooting procedure. Check for omissions and distortions.
• Long-form coherence: Use solution documents of more than 2,000 Chinese characters and check whether terminology and conclusions remain stable throughout.
• Style transfer: Ask the model to present the same material as an internal technical memo and as an explanation for nontechnical readers.
• Correction and compression: Correct awkward sentences and remove redundancy while preserving professional information.
• Boundary-aware refusal: When parameters, performance figures, or business relationships cannot be confirmed, the model should state the uncertainty instead of fabricating details.

Each sample should record the input, expected points, prohibited elements, and scoring rules. After a model upgrade or changes to prompts, temperature, or decoding parameters, run regression tests against the same set.

The claim that “Chinese quality has declined across the board” is meaningful only when a fixed evaluation set shows a consistent regression. Otherwise, the task distribution may have changed, or the sample may simply have exposed a known weakness.

Scoring can combine point coverage with error counts. Recommended error categories include:

• Omitted points
• Fabricated information
• Internal contradictions
• Mismatched tone
• Disorganized structure
• Unstable terminology

Automatic metrics—such as overlap with reference points, format validation, and prohibited-word detection—are useful as quality gates. Fluency and readability still require human sampling. A high automated score does not prove that the Chinese is good; it only shows that the output did not trigger the failure modes encoded in the rules.

A lightweight evaluation record might look like this:

id: zh-write-014
task: Rewrite interface-timeout troubleshooting points for a business audience
must: symptoms, possible causes, user checks, conditions requiring engineering support
forbid: specific vendor API names, unprovided latency figures, claims that the issue has been fixed
score: full score for covering all 4 required points; heavy penalty for each fabricated detail

Optimization: Constraints, Data, and Post-Processing

At the prompt level, define the output contract clearly: Who is the reader? Where should the conclusion appear? Which details must not be guessed? What terminology should be used?

For Chinese technical writing, instructions such as “state the conclusion first, then the conditions, steps, and risks” are usually more effective than a general request to “write professionally.” Complex tasks can be divided into three stages: outline generation, expansion, and self-checking. The self-check should verify required points and contradictions only. Otherwise, the model may introduce new facts while reviewing its own answer.

At the data level, prioritize high-quality Chinese content that matches the target distribution. Useful sources include anonymized real support tickets, internal standards, and published technical documentation. Remove template-like filler and machine-translation artifacts during cleaning; otherwise, the model may learn to produce “correct-sounding empty language.”

Alignment data should include both positive examples and negative examples labeled by error type, so the model can learn which expressions are unacceptable. Do not assume that a larger general-purpose corpus will automatically improve Chinese technical writing. Domain terminology and formal or technical writing styles require targeted samples.

Generation parameters should not be tuned solely for diversity. Explanatory tasks generally benefit from lower randomness and limits on excessive output length, reducing filler and inconsistencies in later sections.

Post-processing can validate formatting, detect prohibited terms, and check consistency in numbers and terminology. However, it should not be expected to repair core factual errors. If such errors are common, the underlying prompts or data need improvement.

When the generation pipeline runs in the cloud, evaluation samples, scores, and failure cases can be stored in object storage, while workflow orchestration can be handled by a workflow engine or message queue. Quality fluctuations can be connected to monitoring and alerting. The key requirement is traceability: the model version, prompt version, generation parameters, and evaluation-set version must be recorded together. Otherwise, it is impossible to explain why a result improved or deteriorated.

Select Models by Scenario, Not by Reputation

No model is likely to be best at every Chinese-language task.

• Short customer-service replies prioritize politeness and constraint adherence.
• Solution writing prioritizes structure and faithfulness.
• Code comments and commit messages prioritize concision and stable terminology.
• Multi-turn conversations prioritize retention of conditions already provided by the user.

Enterprise evaluations should report scores for each subset and the main error types instead of relying on a single aggregate score. If a new version improves creative writing but performs worse under factual constraints, the decision to switch should be based on business priorities. Online canary comparisons should also track user satisfaction, human rewrite rates, and complaint categories.

Capability fluctuations are normal. Changes in training data, alignment objectives, and product strategy can cause performance in a particular writing style to decline. Therefore, “which model is currently best” is meaningful only for a specific evaluation set and point in time. It should not be generalized into a permanent ranking.

From an engineering perspective, the more valuable goals are a stable baseline, early detection of regressions, and a process for feeding failure cases back into prompts or training data.

Common Implementation Pitfalls

Using translation-like reference answers for training can make Chinese writing increasingly resemble sentence-by-sentence translation. Evaluating only literary quality can overlook fabrication, which is often more damaging in business scenarios. Testing only a successful single-turn prompt can hide the loss of conditions in multi-turn conversations.

Asking a model to assess its own “Chinese proficiency” and treating the result as an objective score is circular and unauditable. Comparisons should keep decoding parameters and system prompts fixed; otherwise, the evaluation measures prompt engineering rather than model capability.

Another common mistake is treating brand-experience content as technical evidence. Subjective positive feedback can suggest which types of samples should be added, but it cannot replace explicit scoring rules.

When content involves unpublished interfaces, performance figures, or business relationships, both articles and model outputs should remain cautious. State general industry concepts clearly, but do not invent capabilities.

Conclusion

The quality of Chinese expression should be reflected in semantic faithfulness, clear structure, controllable style, factual consistency, and regression readiness.

A layered task set, standardized error categories, and version-linked evaluations provide a more reliable foundation than claims that one generation is universally superior or that all later models have declined. Prompt contracts, in-domain data, and lightweight post-processing can then be used to improve the system continuously.

When a team manages its evaluation set and failure cases first, it gains a consistent standard for measuring future model changes, prompt adjustments, and production quality.