Evaluating LLM Chinese Expression Capabilities: An Engineering Selection Guide

The question of “Which LLM has the best Chinese expression?” is seemingly simple but actually depends heavily on the specific task. Good Chinese expression goes beyond vocabulary size or mere fluency; it encompasses language accuracy, discourse organization, pragmatic naturalness, style control, factual consistency, professional terminology usage, and long-text stability. Different general-purpose LLMs exhibit distinct tendencies due to variations in training data ratios, alignment strategies, decoding parameters, and context management. Some excel in long-form elaboration and literary expression, others are more balanced and stable, and some are better suited for technical explanations and structured outputs. Discussing the “best” model in isolation from the task often leads to mistaking subjective experience for engineering conclusions.

1. Defining Operational Dimensions for “Good Chinese Expression”

The first step in engineering selection is breaking down vague impressions into evaluable dimensions. It is recommended to cover at least the following seven metrics:

1. Language Accuracy: Basic issues like typos, grammatical errors, improper collocations, punctuation misuse, and ambiguous pronoun references. This is a baseline metric suitable for rule-based validation combined with manual spot-checks.
2. Discourse Coherence: Whether long texts have clear topic sentences, logical progression between paragraphs, consistent concepts, and no context degradation. This is particularly critical for reports, proposals, novels, and course content.
3. Pragmatic Naturalness: Whether the output reads like something a native Chinese speaker would naturally write, avoiding translationese, mechanical parallelism, empty clichés, and over-explanation. Chatbots, customer service, and marketing copy are highly sensitive to this dimension.
4. Style Control: The ability to stably switch tones—such as formal, colloquial, professional, literary, or popular science—according to requirements and maintain consistency throughout long texts. Evaluation should provide a clear reader persona and stylistic constraints.
5. Factual Consistency: Whether numbers, entities, citations, and causal relationships are consistent throughout the text, avoiding content that flows well but drifts factually. Professional content must incorporate fact-checking into the scoring.
6. Professional Expression: Accurate terminology, clear definitions, specified boundary conditions, and executable steps. The weight of this dimension should be increased for technical documentation, analytical reports, and proposal specifications.
7. Stability and Controllability: The quality variance of multiple generations from the same prompt, consistency after segmented long-text generation, and adherence to format and length constraints.

2. Abstracting Style Tendencies Rather Than Simple Ranking

Common general-purpose LLMs often display different tendencies in Chinese expression. Models leaning towards long-form writing often feature natural paragraph transitions and thorough narrative and argumentative development, though they may sometimes be overly restrained or over-explain. Balanced models offer structural stability and clear logic, making them suitable for Q&A, reports, and data organization. Models oriented toward technical explanations excel at deconstructing complex problems but may lack literary appeal. These differences stem from data, alignment, and decoding strategies, meaning they cannot be directly translated into “best for a certain task.” The correct approach is to establish a candidate set and conduct blind comparisons using the same task samples and scoring criteria.

3. Building a Reproducible Evaluation System

An evaluation system should include a sample set, a scoring scale, a blind evaluation process, and statistical aggregation. The sample set should be stratified by task: long-form writing, daily Q&A, technical explanation, professional reports, literary creation, and rewriting/polishing. Each stratum should be further layered by domain, length, style, and risk level. The scoring scale is recommended to use anchored descriptions from 1 to 5, avoiding simple “good/bad” labels. Long-text tasks can be broken down into seven items: topic focus, structural clarity, paragraph transition, language naturalness, information density, factual consistency, and style stability. Chat tasks should increase the weight of pragmatic naturalness, context continuity, and conciseness. Professional content should increase the weight of terminology accuracy, boundary specification, and executability.

Blind evaluation is crucial: hide candidate model identities, have two individuals score independently, route disputed samples to arbitration, and record inter-rater reliability. Automated metrics can serve as auxiliary tools—such as repetition rate, paragraph length distribution, terminology hit rate, entity consistency checks, and style classification—but cannot replace human judgment. For statistics, pay attention to mean, percentiles, variance, and win rate; conclusions from small samples should be treated with caution.

Illustrative evaluation pseudocode:

suite = build_suite(tasks=["long-form", "QA", "tech_explanation", "professional_report"], strata=["domain", "length", "style"])
for case in suite:
    for model in candidate_models:
        outputs[model][case] = generate(model, case.prompt, fixed_params)
scores = blind_rubric(outputs, rubric, reviewers=2)
report = aggregate(scores, weights=task_weights, metrics=["mean", "p50", "variance", "win_rate"])

4. Engineering Selection Process and Implementation Recommendations

The recommended workflow is: requirement breakdown -> metric weighting -> candidate set -> offline blind evaluation -> stress testing -> engineering evaluation -> canary release. In addition to expression quality, engineering evaluation should cover response latency, concurrency stability, context length, content safety, log monitoring, and cost structure. When setting up an evaluation environment on general-purpose model service platforms like Baidu AI Cloud, you can integrate model services, logging services, monitoring alerts, and content safety into unified observability. However, the final decision must still return to task fit.

Regarding prompt engineering, long-form writing is recommended to follow the “outline first, then segment, then consistency check” approach. Professional content should provide a glossary, reader persona, prohibited expressions, and a fact-checking checklist. Rewriting and polishing should specify the style, length, and retained information. Chat scenarios should constrain conciseness and follow-up strategies. Post-processing steps like repetition detection, terminology replacement, fact-checking, and style polishing added after generation can significantly reduce the risk of “fluent but empty” outputs.

In conclusion, there is no absolute “best in Chinese” independent of the task. Enterprises should deconstruct Chinese expression into measurable dimensions and build their own selection evidence chain using stratified samples, blind evaluation scales, and regression testing. For long-form writing, look at discourse and stability; for chat, look at pragmatic naturalness; for professional content, look at terminology and factual consistency. Only by converting subjective experience into reproducible evaluation can you select the LLM Chinese expression capability that truly fits your business needs.