LLM Conversation Style: Moving from Jargon to Natural Expression

Introduction: Why Plain Language Deserves Dedicated Evaluation

In the past, people evaluated large language models mainly by accuracy, reasoning ability, and code pass rates. As models enter customer service, office assistants, teaching, and other scenarios, another metric has become important: whether an answer is easy to read and whether the conclusion is immediately visible. Some teams have reported in internal comparisons or public discussions that the conversational naturalness of new versions has improved. However, these results usually lack a unified test set and a public methodology, making them difficult for readers to verify. Therefore, judging whether a style improvement is real requires reproducible data, not impressions.

1. Where Jargon Accumulation Comes From

The “technical jargon” of early models was not accidental. There are three main reasons.

First, the composition of the pretraining corpus affects output style. Papers, technical documentation, and legal texts have high information density and a large proportion of formal written language. The more a model learns from these texts, the more likely it is to use complete written sentences and specialized terminology when answering questions.

Second, preference training may reward answers that “look rigorous.” If annotators prefer long answers with complete structure and formal wording when scoring, the reward model will amplify these features. As a result, the model learns to turn simple questions into mini-papers.

Third, the style ratio in instruction-tuning data determines the default tone. If training samples are mostly formal explanatory text, the model’s default tone will lean toward explanatory writing.

The following constructed example compares style changes. The user asks: “Why does my Python script raise a KeyError?”

• Old-style response: “According to the hash lookup mechanism of dictionaries, when the requested key does not exist in the mapping structure, the interpreter raises a KeyError exception. It is recommended to check in advance with the in operator or the get method.”
• New-style response: “A KeyError means the dictionary does not contain the key you are looking for. The simplest way to handle it is d.get('name', 'default'); if the key is not found, it returns the default value instead of raising an error.”

Both answers contain the same facts, but the latter gives the conclusion first and then the operation, reducing reading burden. Note that these examples are manually constructed and do not represent measured results from any specific model.

2. Which Stages Usually Produce Style Improvements

Common improvement directions include the following three categories.

• Instruction data diversification: Add samples with colloquial questions, multi-turn follow-ups, and emotional expressions so the model learns to adjust sentence patterns to the user’s tone.
• Preference annotation criteria adjustment: Include readability and conclusion-first in scoring dimensions, rather than evaluating completeness only.
• Long-context training: Enable the model to more accurately identify reference relationships and implicit intent in multi-turn conversations, reducing repeated questions.

Note that specific training ratios are usually not public. To confirm which stage a given improvement comes from, readers should consult the model’s public technical report or design their own comparative experiments.

3. User Benefits: How to Measure Cognitive Load

Cognitive load is difficult to measure directly, but it can be approximated with three proxy metrics:

• First-pass comprehension rate: whether users can state the conclusion after reading once.
• Number of follow-up questions: how many additional questions users ask to clarify the answer.
• Task completion time: the time from asking the question to performing the operation.

In a small internal test, to compare two models, you can have 20 users each complete 10 technical Q&A questions, record the three metrics, and use a paired test to determine whether differences are significant. With a small sample size, conclusions can only serve as preliminary reference.

4. How to Evaluate a Model’s Natural Expression Ability

4.1 Qualitative Scoring Rubric

In addition to accuracy and code pass rates, the following four qualitative dimensions can be added, each scored from 1 to 5.

• Information accuracy: First judge whether facts are correct. Good style cannot offset errors, so this item should be scored first.
• Information robustness: Ask the same question in three expressions of different complexity. If the proportion of consistent conclusions across the three times reaches 90% or higher, mark it as meeting the standard.
• Sentence diversity: Observe whether response structure changes with question type. For example, comparison questions suit parallel structures, and process questions suit step-by-step structures. If all answers use the same template, score low.
• Redundancy: Calculate the proportion of repeated statements and boilerplate with no substantive content. A score of 5 means no repetition and no empty talk; 1 means a large amount of repetition or preamble.

For implementation, use the following plan: prepare 50 questions per dimension, 200 questions in total; have 3 raters independently score each question and take the median; use Cohen’s kappa to test inter-rater agreement, with kappa no lower than 0.6 considered acceptable. Passing criteria can be set as: average score no lower than 4.0 for each dimension, and information accuracy alone no lower than 4.5.

4.2 Multi-Turn Conversation Testing

Single-turn Q&A cannot reflect performance in longer communication. You can construct 100 conversations, each with 8 to 12 turns, and add interference in proportion: 30% of conversations include topic switches, 20% include contradictory questions, and 10% include mid-conversation interruptions.

Scoring should follow three points. First, raters should not know the identity of the model being tested to reduce bias. Second, score turn by turn and observe whether tone remains consistent. Third, specifically measure performance after correction: after the user points out an error, does the model correct it in the next turn while maintaining the original tone?

4.3 Common Misconceptions

Misconception 1: Longer answers are more natural. Length is not equivalent to readability. The redundancy score is designed to prevent this bias.

Misconception 2: A friendly tone means the answer is reliable. A friendly tone may mask factual errors, so style evaluation must be separated from accuracy evaluation.

Misconception 3: Single-turn tests are enough to draw conclusions. Many problems only surface in multi-turn conversations, such as contradictions or forgetting user constraints mentioned earlier.

4.4 How to Choose Appropriate Model Capabilities

When selecting a model, first clarify the business scenario, then determine the metrics that matter most. For example, customer service scenarios place more weight on first-pass comprehension rate and correction performance, while report generation scenarios place more weight on information accuracy and structural compliance. Then use the same test set to test two or three candidate models and compare their scores across metrics, rather than relying only on promotional materials.

5. Business Adaptation: Constraining Style with System Prompts

The same model needs different expression styles in different products. Through system prompts, you can apply a second layer of constraints on top of the model’s default style. The following is a template example for a customer service scenario:

You are an after-sales assistant for an e-commerce platform.
Tone: Conversational; address the user as "you"; avoid technical jargon; if jargon is unavoidable, briefly explain it in parentheses.
Structure: Give the conclusion first, then list 1 to 3 reasons, and finally provide the next step.
Constraints: Clearly state uncertain information; do not fabricate order status; keep each reply under 200 characters.

In report generation scenarios, the constraints should instead focus on output format, such as requiring a fixed heading hierarchy, defining terminology when it first appears, and prohibiting colloquial filler words. After writing the template, validate it with the same test set to confirm that the constraints actually take effect, rather than assuming they do.

6. Risks and Boundaries

Natural expression brings benefits, but also new problems.

• Factual accuracy risk: The more natural the tone, the easier it is for users to trust errors. Accuracy should be tested separately after each version update.
• Over-anthropomorphism risk: If the model appears to have emotions or judgment, users may develop inappropriate dependence. Products should clearly identify AI identity and avoid inducing emotional investment.
• Cost risk: More detailed explanations increase output tokens and response latency. Before launch, calculate the cost per conversation.
• Privacy risk: If the model automatically adjusts tone and information granularity based on history, user consent is needed, and data storage methods should be explained.

Several speculations about future trends deserve attention, but currently all lack sufficient public evidence: business users describing features directly in natural language, combining voice, vision, and text, and models adjusting expression based on user habits. Whether these directions can be realized depends on whether accuracy, cost, and privacy issues can be solved simultaneously.

Conclusion: Judge Style Progress with Measured Data

Improvements in conversational style may provide practical help in scenarios with high comprehension costs. In customer service, teaching, and everyday consultation, answers that give the conclusion first and use less jargon often reduce follow-up questions. However, in legal, medical, and other scenarios, formal written expression remains necessary. To judge whether a model “speaks plainly,” look at its first-pass comprehension rate, correction performance, and factual accuracy in a unified test set, rather than drawing conclusions from tone alone.