Evaluating Chinese Writing Capabilities of Large Language Models: Dimensions, Workflows, and Optimization Practices

Why “Which LLM Is Best at Chinese?” Is the Wrong Question

When selecting a model, many people directly ask, “Which large language model currently has the strongest Chinese writing ability?” The question sounds direct, but it is difficult to answer effectively. There are three main reasons.

First, “Chinese writing ability” is a composite concept. It includes at least grammar accuracy, vocabulary richness, logical organization, stylistic control, and long-range consistency. Different tasks place different weights on these sub-abilities. Advertising copy values language feel and creativity, technical documentation values accuracy and rigor, and long-form novels value narrative structure and character consistency. Using a single yardstick for all of them is not reasonable.

Second, model iteration is extremely fast. Mainstream large language models are often updated in cycles measured by months, so any ranking without a timestamp quickly becomes outdated. A capability that is considered leading today may be matched by many models a few months later.

Third, the same model can perform very differently depending on prompts and sampling parameters such as temperature and top-p. Discussing model quality without specifying the usage context provides little practical reference.

A more professional approach is therefore to first define the task type, then break down task-specific, evaluable metrics, and finally compare candidate models using a unified test set. The framework below follows this idea.

Breaking Down Chinese Writing Capabilities into Evaluable Dimensions

Take structured long-form writing as an example, such as short drama scripts, novel chapters, or business reports. Chinese writing ability can be divided into the following observable and scoreable dimensions:

• Outline planning: Given limited requirements, can the model generate a content skeleton with clear hierarchy, self-consistent logic, and reasonable pacing, including setup, development, turning points, chapter division, and key-point distribution?
• Instruction following: Given an outline or structural constraints, can the model expand strictly according to the required points without omissions, off-topic content, or unauthorized addition or deletion of key information?
• Style consistency: Are tone, person, terminology, and sentence patterns consistent throughout? Can the model maintain a specific genre, such as suspense, comedy, or formal official writing?
• Long-text coherence: Do foreshadowing, character relationships, timelines, and settings remain consistent across paragraphs and chapters, avoiding “memory breaks”?
• Detail control: How accurately does the model follow constraints such as word count, format, proper nouns, and hard plot requirements, such as “a certain character must not appear”?

To make evaluation reproducible, these dimensions can be fixed into a scoring rubric. A 1–5 point scale can be used for each item, with weights assigned to key items:

# Example rubric for Chinese writing capability evaluation
dimensions:
  outline_planning:     { weight: 0.25 }  # Outline planning
  instruction_follow:   { weight: 0.30 }  # Instruction following, highest weight
  style_consistency:    { weight: 0.15 }  # Style consistency
  long_range_coherence: { weight: 0.20 }  # Long-text coherence
  detail_control:       { weight: 0.10 }  # Detail control
score = Σ(dimension_score × weight)      # Maximum score: 5

In practice, it is advisable to prepare 5–10 typical samples for each dimension, have reviewers score them independently, and then take the average to reduce subjective bias. The conclusions are comparable only when all candidate models are evaluated with the same prompts, the same samples, and the same scoring rubric.

A Recommended Workflow for Structured Long-Form Creation

A common mistake in practice is expecting “one-shot perfection”: giving all requirements to the model at once and expecting a finished draft directly. This approach often leads to a high rework rate. A more reliable approach is step-by-step convergence, where the output of each stage is controllable and verifiable:

1. Structure the requirements: Turn vague requests such as “help me write a script” into structured fields, including genre, length, character settings, the core conflict of each episode, word-count range, and other constraints.
2. Generate the outline: First ask the model only for an outline. Review its logic and pacing manually before moving to the next step.
3. Expand one point at a time: Using “outline + detailed requirements for one episode” as input, have the model expand episode by episode or paragraph by paragraph. The smaller the output range, the more stable the quality.
4. Check consistency: Feed completed chapters back into the context, check whether characters, settings, and foreshadowing remain coherent, and then decide whether to revise or rewrite.

Below is an example prompt template for “expanding a single point based on an outline”:

Role: You are an experienced [screenplay/novel] writer.
Task: Expand the body text for "Episode N" strictly according to the outline below.
Hard constraints:
- The body text must be 700–900 words;
- It must include all plot points listed in the outline;
- Character A must not appear in this episode;
- Keep the entire passage in the third person and in a [suspense/comedy] style.
[Global outline]
{{outline}}
[Detailed requirements for this episode]
{{episode_brief}}

This approach, moving in small steps and verifying each step, can significantly reduce the proportion of content that requires manual rework. Errors are isolated early in each stage rather than accumulating and surfacing at the final draft stage.

Why AI-Generated Chinese Often Sounds Formulaic, and How to Reduce It

Many users notice that Chinese text generated by large language models is often “usable, but too formulaic.” This is often described as a “template-like tone.” The underlying cause is that models tend to generate expressions that appear frequently in training data and have high probability. As a result, the wording becomes safe but mediocre. There are three main ways to reduce this problem:

• Specify style and forbidden phrases: Provide a style example directly in the prompt and list clichés to avoid, such as “in summary” or “in this rapidly developing era.”
• Use few-shot examples: Provide 1–3 sample passages that match the target style. This helps the model align its language feel and usually works better than a purely textual description.
• Adjust sampling parameters appropriately: Increasing temperature can improve diversity and reduce formulaic tone, but it should be paired with verification to avoid reduced coherence. For rigorous documents, lowering temperature is preferable to ensure stability.

These methods reduce probability; they do not eliminate the problem. Human review remains indispensable in high-quality output pipelines, especially in scenarios requiring factual accuracy or professional expertise.

Building Your Own Evaluation Test Set

To keep model-selection conclusions useful over time, it is better to build a private test set than to rely on external leaderboards. Key points include:

• Match real tasks: Extract real requirements from historical projects, anonymize them, and use them as test items so that evaluation aligns with actual business needs.
• Cover key dimensions: Design dedicated tasks for each dimension. For example, “check for omissions based on a given outline” can be used to test instruction following.
• Fix the scoring baseline: Use the same rubric and the same reviewers, and rerun tests regularly to create a traceable performance curve.
• Include failure cases: Collect low-quality outputs that have appeared in production and turn them into regression tests to verify whether optimizations are effective.

With this asset, regardless of how models iterate, you can complete a horizontal comparison in about half a day and quickly judge whether a new version is worth switching to.

Enterprise Implementation: From Model Selection to Engineering

When Chinese generation capabilities move into production, single-point prompt tuning is no longer enough. Engineering support is required. Enterprises typically combine the following general capabilities on a cloud platform:

• Model service integration: Call large language model capabilities through a unified API gateway to facilitate gradual rollout and A/B testing across different models.
• Logging and monitoring alerts: Record the inputs, outputs, latency, and failure rate of each call to support quality regression and cost analysis.
• Object storage and message queues: Archive generated results and evaluation data, and asynchronously drive batch evaluation tasks.
• Observability: Combine tracing and metric dashboards to identify timeouts, truncation, or quality fluctuations in long-text generation.

Once the evaluation process is engineered, model selection is no longer guesswork. It becomes a sustainable, traceable, and optimizable data-driven decision.

In short, rather than asking which large language model is best at Chinese, build a complete loop: task breakdown, dimension-based evaluation, stepwise generation, and engineered re-evaluation. With this methodology, regardless of how the underlying models change, you can quickly identify the solution best suited to your Chinese content creation scenario.