Evaluating and Optimizing LLM Chinese Expression Capabilities

1. Shifting from “Which is Best” to “How to Evaluate and Optimize”

“Which large language model has the best Chinese expression capability?” is a common question among developers and enterprise users, but it usually relies on a false premise: that an absolute optimal model exists across all scenarios, tasks, and versions. In practical engineering, Chinese expression is not a single metric. It is a multi-dimensional capability comprising linguistic fluency, factual accuracy, textual structure, stylistic adaptation, instruction following, long-text coherence, terminology consistency, cultural context understanding, and safety compliance. A model excelling in marketing copy does not guarantee leadership in technical documentation, contract summaries, customer service Q&A, or code comments. An apparent “significant lead” in a subjective experience might simply be the result of prompt design, sampling parameters, context length, version routing, or evaluation sample selection.

Therefore, a more reliable approach is to reframe the question: How can we build a reproducible, explainable, and regression-friendly system for evaluating and optimizing Chinese expression? For enterprises, this system should answer three questions: First, which capability dimensions are essential for the current business scenario? Second, is the performance of different candidate solutions stable across these dimensions? Third, how can we quickly detect quality regressions and identify the root causes after model iterations or prompt strategy changes? Only by transforming “subjective feelings” into “observable metrics” can model selection and optimization hold genuine engineering value.

2. Evaluation Dimensions for Chinese Expression

Evaluating Chinese expression requires looking beyond whether it “reads smoothly.” It is recommended to cover at least the following dimensions and assign weights based on business scenarios.

Dimension	Focus	Observable Metrics Examples	Collection Method
Linguistic Fluency	Grammar, collocations, punctuation, cohesion	Readability score, error rate	Double-blind human review
Factual Consistency	Alignment with input materials or common sense	Number of factual errors, hallucination rate	Rule validation, human verification
Textual Structure	Hierarchy, transitions, summary-detail relations	Outline match, paragraph coherence score	Rubric scoring
Style and Tone	Formal/colloquial/technical/marketing style	Style deviation, terminology density	Scenario-based sample review
Instruction Following	Word count, format, role, constraints	Constraint satisfaction rate	Automated rule checks
Long-text Coherence	Consistency, clear references	Repetition rate, number of contradictions	Long-text specific dataset
Terminology Consistency	Unified professional terms, stable definitions	Number of inconsistencies	Glossary comparison
Safety and Compliance	Sensitive content, bias, unauthorized commitments	Violation rate, refusal appropriateness	Safety policy and manual review

Among these, factual consistency and instruction following typically impact enterprise deployment more than “literary grace.” Common issues in Chinese writing, such as “appearing professional but containing factual errors,” “having a complete structure but omitting constraints,” or “inconsistent terminology,” often require a combination of automated rules and human review to be identified. Evaluation datasets should be stratified by business scenario: customer service Q&A leans towards short text and accuracy; technical documentation focuses on terminology and structure; brand content emphasizes style and compliance; contract summaries prioritize factual consistency and risk omission. Samples from different tiers should not be averaged together, as this can mask critical shortcomings.

3. Building a Reproducible Evaluation Pipeline

A reproducible evaluation pipeline should include sample management, prompt templates, generation parameters, automated checks, blind human review, statistical aggregation, and result archiving. It is advisable to fix version information, including the evaluation dataset version, prompt template version, decoding parameters, review rule version, and execution time. For highly subjective dimensions, use double-blind reviews and inter-rater consistency checks; for rule-based constraints—such as word count, format, mandatory terms, and banned expressions—prioritize automated checks.

The pseudocode is as follows:

function evaluate_candidate(candidate, dataset, rubric, params):
    results = []
    for case in dataset:
        prompt = build_prompt(case.input, case.context, rubric, params.template_version)
        output = candidate.generate(prompt, params)
        auto_score = check_constraints(output, case.constraints)
        human_score = blind_review(output, rubric, case.context)  // Double-blind, multiple reviewers
        results.append({case.id, case.scene, auto_score, human_score, output})
    return aggregate_by_scene(results), consistency_report(results)

Pay attention to statistical significance during evaluation. Average score differences on small samples might just be random fluctuations; the variance of the same model under different prompt templates can also be significant. It is recommended to retain sufficient samples for each scenario and report the mean, percentiles, and failure cases. Claims that “capabilities worsened after a certain version” should be verified through regression testing on a fixed evaluation set rather than relying on fragmented user experiences. If the business uses multi-version routing or canary releases, logs should record the actual model version, prompt template, and parameters hit, to avoid mistaking routing differences for changes in model capability.

4. Common Causes of Chinese Expression Fluctuations During Model Iteration

Model iterations do not necessarily bring linear improvements in Chinese expression. Common causes include: changes in training data ratios leading to insufficient coverage of certain stylistic forms; alignment objectives leaning more towards safety, brevity, or multilingual balance, thereby sacrificing some long-form expansion capabilities; enhanced long-context capabilities causing the model to rely more on context and reduce active organization; decoding parameter changes affecting sentence variety; tightened safety policies leading to excessive refusals of normal requests; and evaluation set pollution or prompt template overfitting creating false improvements. Furthermore, subjective experiences are easily influenced by “first paragraph impressions” and “formatting aesthetics.” If reviewers do not conduct blind reviews, they may overestimate or underestimate the true capability.

Therefore, when a team observes fluctuations in Chinese writing quality, they should first attribute the cause: is it a model version change, prompt template change, parameter change, business input distribution change, or a change in review standards? It is recommended to establish a regression set covering high-frequency scenarios, edge cases, and historical failures. Run regressions after each change and compare the distribution of failure types rather than just looking at total scores. For long-text tasks, specific checks like “outline consistency,” “paragraph topic deviation,” and “internal contradictions” can be added; for professional writing, glossary and fact-checking lists can be included.

5. Engineering Optimization Paths

First, structured prompt engineering. Break down the task into role, target audience, writing goals, required information, banned expressions, output structure, word count range, and self-check requirements. For long texts, ask the model to generate an outline first, then expand section by section, and finally unify terminology and facts.

Second, Retrieval-Augmented Generation (RAG). For fact-intensive tasks, retrieve authoritative materials first, then require the model to write solely based on the provided materials and output citation locations or evidence sentences.

Third, terminology and style constraints. Maintain glossaries, style guides, and positive/negative examples. Provide a few high-quality examples in the prompt to reduce stylistic drift.

Fourth, supervised fine-tuning (SFT) and preference alignment. If a general model is unstable on specific business styles, use business data for SFT, then use preference data to optimize structure, accuracy, and compliance. Training data must be deduplicated, desensitized, and quality-reviewed.

Fifth, multi-stage generation and verification. Generate a draft first, then execute fact-checking, terminology consistency checks, formatting checks, and style revisions, followed by a final manual spot-check.

Sixth, canary release and monitoring. After launch, log generation records, constraint satisfaction rates, human feedback, and complaint types, and monitor quality metrics by scenario.

When deploying in the cloud, you can leverage general infrastructure from Baidu AI Cloud to host the evaluation and execution pipeline. For example, use Object Storage to save evaluation datasets and results, Log Service to collect generation and review logs, Monitoring to track quality metrics, and a Container platform to run batch evaluation tasks. These general infrastructure components do not alter the evaluation methodology itself, but they enhance the reproducibility and operability of the evaluation pipeline.

6. Practical Advice and Conclusion

In practice, it is advisable to follow the principles of “scenario-first, layered evaluation, fixed regression, attributive iteration.” Do not pursue a generic conclusion of “best in Chinese”; instead, output scenario-specific conclusions: in customer service summary scenarios, accuracy carries more weight; in technical documentation scenarios, terminology consistency and structural clarity are key; in brand content scenarios, style and compliance are more critical. During model selection, observe both the mean and tail failures, especially the worst performance in high-risk scenarios. Maintain regression awareness during model iterations—any changes to versions, prompts, or parameters should be backed by evaluation records. Ultimately, improving Chinese expression capability is not a one-time selection problem, but a continuous engineering process encompassing data, prompts, retrieval, fine-tuning, review, and monitoring.