Evaluating the Chinese Expressive Capability of Large Language Models: A Technical Practice Guide

1. Why “Best Chinese Expressive Capability” Is Hard to Answer Directly

In engineering practice, many teams begin with the same question: which large language model currently has the best Chinese expressive capability? However, after validating real projects, they usually find that this question cannot be answered by one universal result. The difficulty is not that there are no differences between models, but that “Chinese expressive capability” is a composite capability spanning multiple tasks, scenarios, and dimensions. It is not a static attribute that can be fully measured by a single indicator.

First, different business scenarios place very different demands on Chinese expression. Marketing copy emphasizes tone, rhythm, persuasiveness, and creativity. Customer service answers emphasize accuracy, compliance, and stability. Official document writing emphasizes format conventions, precise wording, and clear structure. Educational tutoring emphasizes explanation, logical progression, and readability. Knowledge-base Q&A emphasizes factual accuracy, supporting evidence, and resistance to hallucination. The same model may perform differently across these tasks. It may be strong in one type of task and show obvious weaknesses in another.

Second, output quality is affected by many system-level factors, including context length, prompt design, example construction, retrieval augmentation, parameter configuration, post-processing strategies, and safety filtering mechanisms. In other words, model capability is only one part of the result. Complete Chinese expressive capability also depends on the quality of application-layer engineering design. Without standardized evaluation and continuous regression testing, judging “which model is best” based only on a few experience cases can easily introduce bias.

Therefore, a more professional approach is not to ask directly, “Which model is best?” Instead, teams should establish a quantifiable, reproducible, and iterable evaluation system for Chinese expressive capability. This allows model selection, tuning, and launch decisions to be based on evidence.

2. Key Dimensions of Chinese Expressive Capability

To evaluate Chinese expressive capability scientifically, it must first be decomposed into multiple observable, verifiable, and optimizable sub-dimensions. The following categories are usually important for enterprise applications.

2.1 Semantic Understanding and Ambiguity Handling

A notable characteristic of Chinese is its strong context dependence, frequent ellipsis, and complex coreference. A model must understand hidden subjects, objects, logical relations, and emotional tone in a sentence. For example, in Chinese customer service scenarios, users often do not provide complete background. They may use short phrases such as “Can this be refunded?”, “How do I handle that one from last time?”, or “It still doesn’t work.” The model must infer from context what “this” refers to, what type of ticket “that one from last time” refers to, and whether “still doesn’t work” expresses a repeated problem or dissatisfaction.

Chinese also contains many synonyms, near-synonyms, idioms, colloquial expressions, abbreviations, and internet-style expressions. A high-quality model should not only understand literal meaning but also recognize the real intent in context. For ambiguous sentences, nested negation, conditional clauses, and complex coreference, a model’s explanation and rewriting ability are often important criteria for distinguishing performance levels.

2.2 Factual Accuracy and Knowledge Usability

Chinese Q&A, knowledge-base retrieval, and intelligent customer service scenarios have high requirements for factual accuracy. If a model generates fluent but factually incorrect output, it cannot solve the user’s problem and may introduce misleading risk. Therefore, evaluating factual accuracy should consider the following questions:

• Can the model correctly understand the key entities and constraints in the question?
• Can it align the answer with existing knowledge, documents, or evidence?
• Can it express uncertainty appropriately when unsure, instead of forcibly fabricating an answer?
• Can it identify outdated information, conflicting information, or missing information?
• Can it provide verifiable evidence or source clues in the answer?

From an engineering perspective, factual accuracy cannot rely only on manual browsing. It also requires automated methods such as evidence comparison, keyword verification, and citation-consistency checks. For high-risk business scenarios, knowledge-base retrieval augmentation should be introduced to reduce uncertainty caused by the model generating content without sufficient grounding.

2.3 Long-Context Understanding and Structure Organization

Tasks such as long-form writing, contract review, report summarization, meeting minutes, and multi-turn dialogue place higher demands on long-context understanding. Many models can accept long text input, but this does not mean they can reliably use all the information. Common problems include forgetting earlier constraints, repeating statements, disorganized paragraph structure, missing key points in summaries, and contradictions in multi-turn dialogue.

When evaluating long-form capability, the following aspects should be examined:

• Can the model keep core information consistent across long text?
• Can it output according to a specified structure, such as overview, bullet points, conclusion, and risk warnings?
• Can it summarize multiple passages rather than simply concatenating them?
• Can it keep the role, goal, and constraints stable in multi-turn dialogue?
• Can it handle cross-references and hierarchical relationships in long text?

For enterprise applications, long-form capability is not only about whether a model can generate long text. More importantly, it is about whether the model can write clearly, accurately, and completely.

2.4 Style, Register, and Cultural Adaptation

Chinese expressive capability is also reflected in style control. Different scenarios require different language styles. For example, brand promotional copy may need rhythm and persuasive power. Internal notices need to be concise and clear. Customer service replies need to be polite and restrained. Technical documents need to be accurate and standardized. Children’s content needs to be simple and easy to understand. Formal documents need to follow conventional expressions.

A high-quality model should be able to adjust register according to instructions, including formality, technical level, emotional intensity, sentence length, forms of address, and degree of politeness. Evaluation should not only check whether content is relevant. It should also determine whether the language style matches the target audience.

Chinese expression also involves cultural context and social conventions. A model should avoid stiff translation-like language, incorrect idiom usage, unnatural honorifics, inappropriate metaphors, and long sentence stacking that does not match Chinese reading habits. A model with strong Chinese expression should make readers feel that the text is “written in Chinese,” not “assembled by a machine.”

2.5 Instruction Following and Constraint Satisfaction

In real business scenarios, users often do not simply say “write a paragraph.” They provide many constraints, such as:

• The output must be in Chinese.
• The length must be within 300 words.
• Exaggerated vocabulary must not be used.
• The output must contain three key points.
• The output must be a list.
• Internal terminology must not be disclosed.
• The content is intended for management reporting.
• The tone must remain objective and neutral.

These constraints may seem simple, but they are easily broken during generation. A model may ignore length limits while trying to enrich content, or it may change original facts while polishing language. Therefore, instruction-following ability should be an important part of Chinese expressive capability evaluation.

When evaluating instruction following, the following indicators can be checked:

• Does the format fully meet requirements?
• Is the length within the specified range?
• Are required fields or modules missing?
• Are forbidden words or sensitive expressions violated?
• Are the specified role and tone maintained?
• Is content arranged in the required order?

For automated systems, this capability is especially important because if output cannot be parsed stably, downstream processes will be directly affected.

2.6 Safety, Compliance, and Value Alignment

Evaluating Chinese expressive capability cannot ignore safety and compliance. When generating content, a model should avoid producing illegal content, privacy-infringing content, discriminatory expressions, false facts, malicious guidance, or inappropriate values. In sensitive domains such as finance, healthcare, law, and education, the model should also be able to identify risks and prompt users to seek professional confirmation.

Safety is not an optional appendix outside expressive capability. It is an important prerequisite for Chinese expressive capability to be used in production. A model with strong expression but poor safety cannot be used in real business. A safe model must also express refusals or risk warnings in a natural, clear, and understandable way.

3. Common Evaluation Pitfalls

When evaluating Chinese expressive capability, many teams fall into the following pitfalls.

3.1 Relying on a Few Examples

A few examples are often accidental. A good generation result once does not mean the model is stable across similar tasks. A stiff expression once does not mean the model is weak overall. Chinese expression is significantly affected by prompts, context, sampling strategies, and task complexity. A stable conclusion requires a scaled evaluation set.

3.2 Comparing Only General Writing Quality

General writing is only one part of Chinese capability. Enterprises usually care more about usability in business scenarios, such as whether customer service replies are compliant, whether knowledge Q&A answers are evidence-based, whether official documents follow format rules, and whether marketing copy matches brand tone. If the evaluation set is disconnected from business scenarios, the results will be difficult to use for real model selection.

3.3 Relying Only on Automatic Metrics

Automatic metrics are suitable for objective dimensions such as format, length, keyword coverage, and semantic similarity. However, they have limited judgment on subjective dimensions such as style, logic, persuasiveness, and cultural adaptation. For high-quality Chinese expression, human evaluation remains necessary. The ideal approach is to combine automatic metrics with human scoring.

3.4 Ignoring Evaluation-Set Version Management

Evaluation sets need continuous updates. If a fixed set of questions is used for a long time, models may gradually adapt to the evaluation method while real user questions continue to change. Teams should establish mechanisms for regular supplementation, hard-case mining, error attribution, and version archiving to keep evaluation results effective over time.

3.5 Ignoring the Impact of System Prompts and Context

The same model may perform significantly differently under different system prompts, examples, and context organization. Evaluation should try to unify prompt strategies, context length, output format, and sampling parameters. Otherwise, engineering differences may be mistaken for model-capability differences.

4. A Practical Evaluation Framework for Chinese Expressive Capability

To obtain more objective conclusions, teams can build an evaluation system covering data, metrics, processes, and engineering platforms.

4.1 Build a Layered Evaluation Task Set

The evaluation task set should reflect real business distribution rather than only a few ideal examples. It can be layered according to the following dimensions:

• By task type: Q&A, summarization, rewriting, generation, classification, extraction, translation, multi-turn dialogue, and structured output.
• By text length: short sentences, paragraphs, long articles, and long multi-turn contexts.
• By language style: colloquial, written, formal, marketing, technical, and educational.
• By difficulty: basic expression, complex logic, professional knowledge, and high-constraint tasks.
• By risk level: normal content, sensitive content, and compliance-review content.

Each task category should have clear inputs, reference answers, or scoring points, and desensitized real samples should be retained. For open-ended generation tasks, a single standard answer may not be necessary, but clear scoring rules are required.

4.2 Design Multi-Dimensional Scoring Metrics

Chinese expressive capability should not be represented by only one total score. It should be decomposed into multiple explainable metrics. For example:

• Semantic consistency: Does the answer understand and respond to the user’s real intent?
• Factual accuracy: Is the content consistent with knowledge sources or evidence?
• Logical completeness: Do paragraphs progress reasonably?
• Language fluency: Are there grammatical errors, repetitions, or stiff expressions?
• Style match: Does the output match the target register and brand tone?
• Instruction-following rate: Does it satisfy constraints on length, format, role, and forbidden words?
• Structural compliance: Are titles, lists, fields, or paragraphs organized as required?
• Safety risk rate: Does it contain sensitive, non-compliant, or misleading content?

Different business scenarios should use different weights. For example, customer service scenarios should increase the weights of factual accuracy and compliance. Brand copy scenarios should increase the weights of style match and language fluency. Structured data generation scenarios should increase the weight of format compliance.

4.3 Establish Human Scoring Guidelines

Human evaluation needs standardization to avoid inconsistent standards among evaluators. The following practices can be used:

• Use a unified scoring sheet and divide each dimension into clear levels.
• Desensitize samples and hide model names to avoid bias.
• Display outputs in random order to reduce position bias.
• Use multiple evaluators for key samples and calculate consistency.
• Review samples with major disagreement or use expert adjudication.
• Keep scoring reasons for later error attribution.

Human scoring is not simply assigning a number. It should record problem types such as “factual error,” “inappropriate tone,” “missing format,” “broken logic,” and “unmet instruction.” These structured labels are useful for later optimization.

4.4 Example of an Automated Evaluation Pipeline

Once a team has sufficient engineering capability, the evaluation process can be automated. The following is an illustrative flow:

Read the evaluation task set
Read each task sample
Call the model under test to generate output
Perform basic post-processing on the output
Compute automatic metrics
Run safety and compliance checks
Aggregate scoring results
Generate evaluation report
Mark abnormal samples

The process can also be expressed with the following pseudocode:

evaluation_set = read_task_set()
result_list = []
for task in evaluation_set:
    prompt = construct_prompt(task)
    output = call_model_under_test(prompt)
    output = basic_cleaning(output)
    scores = {
        semantic_consistency: compute_semantic_similarity(output, task.reference_answer),
        format_compliance: check_format(output, task.constraints),
        length_compliance: check_length(output, task.constraints),
        factual_consistency: verify_evidence(output, task.evidence),
        safety_risk: run_safety_check(output)
    }
    result_list.append(task, output, scores)
report = aggregate_scores(result_list)
output_report(report)

In engineering implementation, evaluation tasks should be decoupled from online business. Offline evaluation is used for model selection and version regression, while online canary testing is used to validate performance under real traffic. Combining both helps balance objectivity and business reality.

4.5 Statistical Significance Checks

Evaluation results should not rely only on average scores. Stability also matters. When different models have close scores, teams need to determine whether the difference is statistically meaningful. The following methods can be used:

• Sample the same task multiple times and observe output stability.
• Compare different versions of the same model for regression testing.
• Calculate win rates for paired samples.
• Compute confidence intervals for key metrics.
• Analyze abnormal samples for root causes.

If two models have similar total scores, but one is clearly more stable in long-form writing, factual accuracy, and instruction following, the more stable model may still be preferred for business selection.

5. Engineering Methods for Improving Chinese Expressive Capability

The purpose of evaluation is not only ranking. More importantly, it is to identify optimization paths. Chinese expressive capability can be continuously improved through engineering methods and does not depend entirely on the model itself.

5.1 Prompt and Context Design

Prompts are the bridge between business requirements and model capability. A high-quality prompt should include the following elements:

• Clear role: Specify what identity the model should use.
• Clear goal: Specify the problem the output should solve.
• Clear audience: Specify who will read the output and what tone should be used.
• Clear constraints: Include length, format, forbidden words, and required items.
• Clear examples: Provide reference examples for complex tasks.
• Clear boundaries: Specify how to handle uncertain information.

For example, in a customer service scenario, the prompt may require the model to “answer only based on the knowledge base; state clearly if no evidence is found; do not fabricate policies; keep the reply within 200 words; use polite and concise Chinese.” The more specific the constraints, the more stable the output.

5.2 Retrieval Augmentation and Evidence Constraints

For knowledge-intensive tasks, relying only on model parameters is often insufficient. Retrieval augmentation allows a model to generate content based on external knowledge, improving accuracy and explainability. In practice, teams should focus on:

• Whether the knowledge base is reasonably chunked.
• Whether retrieval results are relevant.
• Whether evidence is sufficient to support the answer.
• Whether the model actually uses the evidence instead of ignoring it.
• Whether the answer can mark sources or evidence.
• Whether conflicting evidence can be handled effectively.

For Chinese knowledge bases, special attention should be paid to terminology consistency, synonymous expressions, document versions, and context completeness. If retrieval quality is insufficient, even a model with strong expressive capability may produce incorrect answers.

5.3 Fine-Tuning and Preference Alignment

When a general model cannot reliably meet business expression requirements, fine-tuning can be used to improve Chinese capability for specific scenarios. Fine-tuning data should prioritize quality over quantity. It should focus on:

• High-quality instruction samples.
• Typical business Q&A pairs.
• Style-specific writing samples.
• Correction of difficult and error-prone samples.
• Preference ranking data.
• Safety and compliance samples.

During fine-tuning, teams should avoid overfitting to the evaluation set. The evaluation set should be independent from the training set and updated regularly. General-capability regression tests should also be retained to prevent degradation in other scenarios after optimization for one scenario.

5.4 Parameter Control and Output Stability

Sampling parameters affect the stability of Chinese output. For formal business scenarios, teams should not overemphasize random creativity. Accuracy, stability, and reproducibility should be prioritized. During evaluation, temperature, maximum generation length, stop conditions, and retry strategies should be unified. If the same task produces highly variable results across multiple runs, teams should analyze whether the problem comes from ambiguous prompts, insufficient task constraints, or model instability.

5.5 Safety Review and Post-Processing

Chinese expressive capability must eventually enter business systems, so safety review is essential. Common practices include:

• Sensitive-information detection on the input side.
• Compliance checking on the output side.
• Blocking high-risk content.
• Desensitizing privacy information.
• Manual spot checks.
• Feeding abnormal samples back into the evaluation system.

Post-processing should not destroy the original meaning or over-filter content to the point that expression becomes unnatural. The ideal approach is to embed safety policies into evaluation metrics rather than relying only on temporary fixes before launch.

6. Engineering Implementation on Cloud Platforms

In model application practices hosted on Baidu AI Cloud, general-purpose product categories can be used to build an evaluation and governance system. For example, evaluation datasets can be archived in object storage for version management and access control. Offline evaluation tasks can be distributed through a message queue to improve throughput and task decoupling. Model invocation logs can be aggregated into a log service for problem tracing and auditing. Evaluation metrics can be connected to monitoring and alerting to detect issues such as declining format-compliance rates, abnormal average generation length, increased latency, or fluctuating human ratings.

This engineering approach makes Chinese expressive capability a continuously observable, continuously regression-tested, and continuously optimizable system metric rather than a one-off experience result. Development teams can host evaluation services on a container platform, manage samples with a unified data format, and drive iteration through automated reports. Even when model versions change, teams can quickly determine whether a new version meets business requirements in key scenarios.

7. Enterprise Model Selection Recommendations

In real selection, teams should not pursue an abstract “strongest model.” Instead, they should build a scoring matrix around specific scenarios. The following approach can be used:

• Identify the core task: Q&A, writing, summarization, customer service, education, or structured output.
• Determine key metrics: accuracy, compliance, style, speed, cost, and stability.
• Build scenario-based evaluation sets: prioritize real business samples.
• Run offline regression: compare models on similar tasks.
• Conduct small-traffic canary testing: observe real user feedback and online anomalies.
• Establish continuous evaluation mechanisms: regularly update samples and scoring rules.
• Avoid one-time decisions: adjust model selection as business needs change.

For low-error-tolerance scenarios, prioritize solutions with stronger factual accuracy, compliance, and stability. For creative scenarios, appropriately increase the weights of style match and expression diversity. For structured-output scenarios, focus on verifying format constraints and field completeness.

8. Conclusion

Chinese expressive capability is not a simple leaderboard problem. It is a systems engineering problem that depends on tasks, data, prompt engineering, retrieval augmentation, safety and compliance, and continuous evaluation. Facing different large language models on the market, enterprises should not judge quality based only on a few experience cases. They should build a quantifiable evaluation framework and examine models across semantic understanding, factual accuracy, long-form organization, style control, instruction following, and safety compliance.

A reliable selection method is to build an evaluation set around the company’s own business, continuously validate models using a combination of automatic metrics and human scoring, and use engineering methods to ensure output stability, safety, and traceability. Only then can Chinese expressive capability move from subjective impressions to manageable, optimizable, and deliverable technical metrics.