Evaluating LLM Chinese Generation Capabilities: Methods and Writing Practices

Shifting the Question from “Who Writes Better” to “How to Measure Quality”

When discussing the Chinese generation capabilities of large language models (LLMs), it is easy to fall into subjective rankings. A more valuable question for engineering teams is: Under specific tasks, target audiences, and length constraints, is the output accurate, coherent, natural, and reproducible? Chinese generation has its own unique challenges: punctuation and rhythm, referential omission, the boundary between written and spoken language, the abuse of idioms and clichés, information density in long sentences, and “translationese.” Breaking these down into observable metrics is more actionable for technical work than vaguely claiming “strong expressive power.”

The following discussion does not bind to any specific model brand; instead, it provides a general evaluation and improvement methodology. Cloud inference, prompt orchestration, logging, and manual review can all be supported by general capabilities like object storage, message queues, container platforms, and monitoring systems. The focus is on the process, not the details of a specific interface.

What Capabilities Does Chinese Generation Actually Include?

For engineering purposes, it is recommended to break this down into at least six layers to avoid masking defects with a single “writing style” score.

1. Linguistic Correctness: Typos, punctuation, grammatical errors, improper collocations, and inconsistencies in numbers and units. This is the baseline, often masked by fluency in long texts.
2. Information Fidelity: Whether key facts are added or omitted, whether uncertainty is stated as certainty, and whether empty words are used to pad length. In technical documentation, an elegant text with factual drift is more dangerous than a stiff one.
3. Structure and Coherence: Do paragraphs have claims, evidence, and conclusions? Do subheadings actually create hierarchy rather than repetition? Can pronouns be traced back to their subjects?
4. Naturalness: Does it read like a native author writing, rather than aligning syntactically with another language? Typical red flags include overusing “conducted” or “aimed at,” mechanical parallelism, and starting every paragraph with the same sentence structure.
5. Audience Match: The terminology density should differ when addressing developers, business stakeholders, and end-users. Impact comes from rhythm and specific details, not piling up adjectives.
6. Task Fit: The criteria for a “good” output differ among explanatory texts, incident post-mortems, release notes, and marketing copy. Long-form writing also requires examining whether the middle sections collapse or the conclusion spins its wheels.

First-tier intuition often comes from the combined impression of the last four items, but selection or launch should be based on repeatable scoring. Style differences objectively exist: some outputs are more restrained and suitable for specifications; others have a narrative rhythm suitable for external long-form pieces. The difference itself is not a defect; the defect is a mismatch between style and task, or excessive quality fluctuation under the same prompt.

Designing a Repeatable Evaluation Instead of a Single Chat Impression

Task Set

Cover at least four categories, preparing 8–15 real or anonymized requests for each, rather than just testing “write an article about artificial intelligence.”

• Technical Explanation: Write interface behaviors, error code meanings, and rollback steps into readable documentation, requiring no fabricated parameters.
• Long-form Argumentation: Over 1500 words, given an outline and mandatory factual points to retain.
• Rewriting and Compression: Condense lengthy meeting minutes into a decision checklist, without losing responsible parties and deadlines.
• Style Control: Write the same material as both an internal memo and an external announcement, checking whether the tone crosses boundaries.

Each sample should have a fixed input: background, audience, length, forbidden expressions, and mandatory facts. Without constraints, the model will fill in the blanks with its default style, rendering comparisons meaningless.

Scoring Rubric

A 5-point scale is recommended, with independent scoring per dimension and no initial total score. Example dimensions: Correctness, Fidelity, Structure, Naturalness, Specificity, Task Fit. Naturalness can be further broken down into “cliché density,” “sentence length variation,” and “referential clarity.” Two-person blind grading; items with a difference of more than 2 points go to a third arbitrator. Record temperature, max length, and system prompt version, otherwise it cannot be reproduced.

The illustrative aggregation logic can be simple; the key is that weights change with the scenario rather than pursuing a complex formula:

score = 0.25 * fidelity
      + 0.20 * correctness
      + 0.20 * structure
      + 0.20 * naturalness
      + 0.15 * audience_fit
# Technical docs can increase fidelity and correctness to 0.35 / 0.25
# External long-form can increase naturalness, but fidelity must not fall below a threshold, e.g., 4

If fidelity falls below the threshold, no matter how high the naturalness is, it should not be a candidate. This prevents samples with “good writing but fabricated interfaces” from being misjudged as excellent.

Automated Signals Only Filter, Never Make Final Decisions

Available weak signals include: repeated n-gram ratio, paragraph length variance, forbidden word hits, and fact checklist coverage (comparing against a list rather than letting another model act as a free-roaming judge). Automated scores are suitable for regression: change a prompt or post-processing step, and see if the distribution degrades. Ultimately, humans still need to read the middle sections of long texts, because Chinese that is “correct but boring” or “fluent but concept-swapping” is hard to catch with surface-level statistics.

Where Do Long-form Naturalness and Impact Come From?

Long-form failures are usually not due to a lack of vocabulary, but three structural issues.

Unanchored Information. Every paragraph only has opinions without examples. The impact of technical writing often comes from a specific failure scenario: what the user saw, what the system logged, and how to verify the next step. Without scenarios, the more adjectives there are, the more it reads like a press release.

Monotonous Rhythm. Consecutive paragraphs all use “first, second, last,” or every sentence is very long. Native writing alternates between short sentences for conclusion and slightly longer sentences for explanation. During evaluation, you can flag whether three consecutive paragraphs have isomorphic sentence structures.

Clichés Replacing Judgment. Words like “empower,” “leverage,” “closed-loop,” and “build” should be judged as low specificity if they cannot be replaced with actions and objects. Impact is not lyrical expression; it is the reader sensing that the author made trade-offs: what not to do, and what risks to clearly state.

When rewriting, using a three-part “Fact—Meaning—Action” structure is more effective than asking the model to “write with more literary flair.” Literary instructions often increase parallelism and metaphors while simultaneously adding untrue details.

How to Stabilize Generation Quality on the Application Side

Good text in a single chat does not equal good text in a product. It is recommended to treat writing as a pipeline.

Prompt Layering. The system layer should only contain stable rules: audience, factual boundaries, prohibition of fabricating parameters and data, and explicitly writing “needs verification” when uncertain. The user layer holds the current material. Do not pile all style requirements into a single “please be professional and impactful” prompt.

Outline First. For long texts, first generate section headings and the mandatory fact IDs for each section; verify them manually or via rules before expanding. This reduces repetition and going off-topic in the latter half of the text.

Local Regeneration. Only rewrite paragraphs with low scores, and pass the terminology glossary back into the context to avoid referential drift.

Post-processing. Standardize terminology, check if mandatory facts are still present, and remove obvious clichés. Post-processing rules should be explainable, such as “the same term must not have two translations,” rather than secretly swapping it for another PR tone.

Sampling and Regression. Write inputs, prompt versions, outputs, and manual scores into logs. Use monitoring to watch for length anomalies, rejection rates, and repetition rates. Store samples in object storage for comparison after prompt changes. Do not leave users’ original online text in plaintext long-term; anonymization and retention periods should follow compliance requirements.

If a team uses cloud-based inference and surrounding components like Baidu AI Cloud, they should also manage model calls, prompt versions, and evaluation samples separately: inference handles generation, while evaluation sets and scoring records are stored independently to prevent the inability to trace “it felt good online.” This emphasizes architectural separation without assuming any unpublicized interfaces or performance figures.

Common Misjudgments

Mistaking fluency for correctness. Chinese long sentences can be very smooth while the subject has already shifted to a different person or system.

Extrapolating conclusions from English evaluation sets to Chinese. Syntax, honorifics, heading habits, and paragraph density are all different, requiring a separate Chinese task set.

Only testing short answers. Short answers cannot reveal mid-section collapse and front-back contradictions, while long-form writing differences mainly appear after 800 words.

Letting the model give itself high scores for acceptance. Self-scoring can be used to spot obvious grammatical errors but cannot replace a human holding a fact checklist.

Relaxing factual constraints for “naturalness.” Once external text is supplemented with non-existent customer cases or metrics, an expression problem turns into a compliance problem.

Implementation Sequence Suggestions

In the first week, fix the task set and scoring rubric, test the current default prompt first, and establish a baseline distribution. In the second week, change only one lever, such as outline-first or terminology glossary, to see if naturalness rises without fidelity dropping. In the third week, write low-scoring types as negative examples into the prompt, and use automated repetition rates for regression. After launch, sample a fixed proportion of long texts weekly; if scores drop, roll back the prompt version.

This way, Chinese generation capability transforms from a matter of taste debate into a maintainable quality attribute: correct, faithful, structured, reading like it was written by a human, and stable for your audience and length. Style differences can be retained, but the selection criteria should be written in the scoring rubric, not in the impression of a single chat.