Evaluating and Selecting LLMs for Chinese Language Expression: A Practical Engineering Guide

In content creation teams, “Which large model has the best Chinese language expression?” is a very common question. Short-form drama screenwriters, new-media writers, product copywriters, and developers all want a direct answer. From an engineering perspective, however, the question itself needs to be reframed. Chinese language expression is not a single score. It includes at least language correctness, semantic accuracy, style control, long-text consistency, factuality, and safety compliance. Different tasks assign very different weights to these dimensions. A more rigorous question is therefore: under given task, register, length, cost, latency, and compliance constraints, which candidate model is more suitable? This article does not discuss specific product leaderboards. Instead, it provides a reproducible evaluation and selection method that helps teams turn subjective experience into an iterative engineering loop.

1. From “Best” to “Best Fit”: Redefining the Question

In frontline creative writing work such as short-form drama screenwriting, teams often keep multiple candidate models and route tasks such as outlines, character bios, episode outlines, dialogue, and polishing to different models. This experience is valuable because it shows that model capabilities differ by task. Some models are better at short dialogue and everyday expression, some are better at structured outlines and long-form continuation, and some are more stable under style constraints. But “the team uses it often” does not directly mean “it has the best Chinese.” It may also be affected by price, speed, API stability, historical habits, and accumulated prompt experience within the team.

The first step in selection is therefore to break the vague question into a task matrix. It is recommended to build a candidate task list based on “task type × register and style × input length × output length × constraint strength.” For example, task types can be divided into creative generation, rewriting and polishing, structured summarization, fact checking, and multi-turn revision. Register and style can be divided into colloquial, classical, workplace, suspense, and comedy. Input length can be divided into a single paragraph, thousand-character level, and ten-thousand-character level. Constraint strength can be divided into free creation, format constraints, prohibited-word constraints, and character-voice constraints. Only when models are compared under the same task matrix do their differences become interpretable.

2. Six Evaluable Dimensions of Chinese Language Expression

First, language correctness. This includes grammar, word choice, collocations, punctuation, measure words, idioms, and register consistency. For screenwriting scenarios, language correctness is not just “no grammatical errors.” It also means avoiding a written-style register that conflicts with a character’s identity. For example, lower-class characters should not frequently use overly formal expressions. During evaluation, metrics such as typo rate, grammatical-error rate, and register-deviation rate can be set, with manual spot checks for confirmation.

Second, semantic accuracy and instruction following. Does the model understand the task goal, character settings, length limits, prohibited items, and output format? Strong Chinese expression does not equal strong instruction following, but in production both must be satisfied. You can measure constraint satisfaction rate, format compliance rate, and multi-turn revision hit rate.

Third, style and creativity. This includes language feel, rhythm, character voice, humor, suspense setup, and style transfer. Such capabilities are difficult to measure with a single automated metric and are better suited to double-blind human review and pairwise comparison. During review, do not only ask “Is it good?” Instead, provide a fine-grained rubric, such as “character voice consistency,” “dialogue naturalness,” “plot progression efficiency,” and “avoidance of prohibited expressions.”

Fourth, long-text consistency. Long-form content needs to maintain consistency in character settings, timeline, foreshadowing, forms of address, and worldbuilding. During evaluation, construct long-context tasks and check whether the model forgets earlier constraints, repeats settings, or creates conflicts in character relationships. For short-form drama, consistency between episode outlines and character bios is especially important.

Fifth, factuality and safety compliance. Creative writing also requires factual boundaries. For example, industry background, legal common sense, and historical settings must not be obviously wrong. At the same time, evaluate sensitive content boundaries, copyright risk, biased or discriminatory expression, and misleading information. Safety compliance is not “the more conservative, the better.” Instead, it means maintaining stable and explainable refusal and rewriting behavior within the range allowed by the business.

Sixth, engineering attributes. Latency, throughput, cost, stability, observability, and data management capabilities are not part of “Chinese expression,” but they directly affect selection. A candidate model with excellent expression may still be unable to support high-frequency creation if its responses are too slow or its cost is too high. These attributes should be scored as independent dimensions rather than mixed into language scoring.

3. How to Build an Evaluation Set for Creative Writing

The evaluation set should come from real business scenarios but must be de-identified. Samples can be extracted from historical scripts, user feedback, and editor revision records to form a structure of “original input—expected constraints—reference output—scoring points.” Samples should cover both simple tasks and hard cases. Simple tasks include single-sentence polishing. Hard cases include multi-character dialogue, implicit style constraints, long-form continuation, and conflicting instructions. Do not expose the evaluation set in prompts, to avoid overfitting by candidate models.

It is recommended to build the evaluation set by task layers: outline generation, character bios, episode outlines, dialogue creation, style rewriting, fact checking, and multi-turn revision. Set 30 to 100 samples per layer, with the specific size determined by business confidence requirements. Use a 1-to-5 rubric for scoring and retain reviewer comments. To reduce subjective bias, use double-blind review, random order, and pairwise comparison. Conduct multi-reviewer evaluation for key samples and calculate scoring consistency. Automated metrics can assist in measuring constraint satisfaction rate, format compliance rate, repetition rate, length deviation, and fact-check pass rate, but they cannot replace human judgment.

4. Evaluation Workflow and Pseudocode

A reproducible evaluation workflow includes: preparing candidate models, loading evaluation tasks, unifying parameters, collecting outputs, running automated checks, conducting human review, aggregating statistics, and regression retesting. The key is to use the same prompt template and the same parameter range for the same task, avoiding distorted conclusions caused by “unfair prompts.” The following pseudocode illustrates the process:

candidates = load_candidate_models()
tasks = load_eval_tasks()  // includes prompt, constraints, rubric, reference points
results = []
for model in candidates:
    for task in tasks:
        output = call_model(model, task.prompt, task.params)
        auto = check_constraints(output, task.constraints)
        results.append({
            model: model,
            task_id: task.id,
            output: output,
            auto_score: auto
        })
pair_samples = sample_for_human_review(results, strategy="stratified")
human_scores = double_blind_review(pair_samples, rubric)
summary = aggregate(results, human_scores, confidence_interval=True)
report = compare_by_task_and_dimension(summary)

Automated checks can include: whether length exceeds limits, whether required format fields are complete, whether prohibited words appear, whether character names are consistent, whether there are conflicts with prior settings, and whether the proportion of repeated paragraphs is abnormal. Human review focuses on language feel, character voice, rhythm, and creativity. When analyzing statistics, do not look only at average scores. Also examine variance and the distribution of failure samples. A model with a slightly higher average score but large fluctuations in long-text consistency may not be suitable for long-form tasks. Another model with a medium average score but high dialogue naturalness may be more suitable for storyboard dialogue.

5. Engineering Selection and Quality Gates

For engineering selection, it is recommended to use a “candidate pool + task routing + quality gates” approach. Keep multiple models in the candidate pool and route by task: outlines and structured summaries go to routes with stronger constraint following, dialogue and polishing go to routes with more natural language feel, and long-form continuation goes to routes with better long-context consistency. Routing policies should be configurable to support gradual rollout, adjustment, and rollback.

Quality gates are divided into automated gates and manual gates. Automated gates check format, length, prohibited items, repetition rate, and obvious factual errors. Manual gates sample high-risk tasks at a specified ratio. All evaluation data, prompt versions, model outputs, and review records should enter a unified data management process. General cloud object storage can be used to store evaluation samples and outputs, logging services can record calls and review trails, and monitoring and alerting can observe latency, error rates, and cost fluctuations. These general capabilities help upgrade “individual experience” into a “team asset.”

For cost and latency optimization, use batch processing, caching, concurrency control, and tiered calls: low-risk first drafts use a low-cost route, while high-risk final drafts use a high-quality route and add human review. Prompts should be versioned. After changes, regression retesting is mandatory to avoid one prompt optimization degrading other tasks. For compliance, implement data de-identification, access control, audit trails, and copyright risk screening. This is especially important for script content involving unpublished ideas, where data usage boundaries should be clearly defined.

6. Practical Points for Prompting and Style Control

Creative writing prompts should include role, goal, audience, register, length, structure, prohibited items, and examples. Do not only say “write better.” Give checkable constraints. For example:

Task: dialogue polishing
Goal: preserve character relationships and plot information, improve the naturalness of spoken language
Register: modern urban, conversational, minimal written-style phrasing
Length: no more than 500 characters
Prohibited: no preaching, no new key characters, no timeline changes
Output fields: polished text, revision notes, risk notes

Style control can use “a small number of examples + scoring points + negative examples.” Examples anchor language feel, scoring points constrain review, and negative examples show unacceptable expression. During multi-turn revision, ask the model to restate the constraints before producing output, reducing forgetting. For long-form tasks, generate in stages: first character bios, then episode outlines, and finally dialogue, with consistency checks at each stage.

7. Common Pitfalls and Conclusion

Common pitfalls include: looking only at single-turn chat experience; looking only at public leaderboards; ignoring task distribution; comparing different candidates with different prompts; ignoring long-text consistency; ignoring cost and latency; and turning human review into “gut-feeling scoring.” These practices make selection conclusions unstable. A more reasonable approach is to build a small but highly representative evaluation set, fix the process, use double-blind review, calculate confidence intervals, and conduct regular regression testing.

The conclusion is that there is no “absolute best” independent of the task. For Chinese language expression, teams should build a closed loop of “task—dimension—metric—review—gate.” For creative writing scenarios such as short-form drama screenwriting, it is recommended to retain multiple candidates, route by task, strengthen style constraints and human review, and incorporate evaluation data and prompt versions into engineering management. This is how teams can achieve an explainable and reproducible balance among Chinese expression quality, creative efficiency, cost, and compliance.