Why Large Language Models Misidentify Themselves: Insights from Distillation and Training Data

I. The Nature of the Problem: A Model’s Self-Claim Does Not Equal True Identity

When a large language model (LLM) claims “I am model X” during a conversation, many people intuitively interpret this as the model possessing a stable self-awareness, or even use it as evidence that it shares weights, training data, or inference systems with model X. From a technical perspective, however, this understanding is imprecise.

The core task of an LLM is to predict the most likely subsequent text based on the context. It does not read its own model version through an independent, reliable identity module. Instead, it generates a probabilistically reasonable response by synthesizing system prompts, conversation history, training corpora, response styles, and the current query. Therefore, “I am model X” is often merely the result of language generation and cannot directly prove the model’s true identity.

A model might misidentify its identity in the following scenarios:

1. A large volume of Q&A about a specific model exists in the training corpus;
2. A certain response style is widely associated with a specific model by the public;
3. The conversational context implies a certain identity, even if the prompt does not explicitly state it;
4. The model learned inaccurate identity statements from a massive amount of synthetic data;
5. Server-side system instructions, routing strategies, or testing environment information are not visible to the user.

Thus, a model’s self-reported identity can only be observed as a phenomenon; it cannot serve as direct evidence of weight origin, training relationships, or technical attribution.

II. Why Different Models Exhibit Similar Expression Styles

Similarities in responses across different models are usually caused by multiple factors and cannot be simply attributed to “Model A copied Model B.”

1. Convergence from Public Corpora

Internet text, technical documentation, Q&A content, and public code inherently contain a lot of duplication. After different models are pre-trained on similar public data, they may naturally learn similar expression patterns, argumentation structures, and common terminology.

For example, when faced with a question like “How to explain a complex technical concept,” many models adopt a “define first, then categorize, and finally summarize” structure. This does not mean there is a direct distillation relationship between them, but rather that this structure appears frequently in the training corpus.

2. Format Unification via Instruction Tuning

The post-training phase typically reinforces the model’s instruction-following capabilities, including:

• Stating the conclusion first;
• Using bullet points for explanations;
• Avoiding overly absolute statements;
• Adding disclaimers;
• Refusing or rephrasing sensitive questions;
• Adopting a more polite and comprehensive expression.

Since many models are optimized with similar objectives, they may become increasingly similar in surface style.

3. Secondary Convergence from Synthetic Data

When Q&A, summaries, reasoning processes, and code generated by a model are reused to train other models, the original model’s expression habits can propagate through the data pipeline. After multiple rounds of generation, filtering, and retraining, multiple models may exhibit similar phrasing and structures.

However, observing similar outputs only indicates a statistical correlation. It cannot independently prove a specific data source, let alone confirm that one model completely replicated another.

III. What is Model Distillation?

Model distillation refers to using a more capable teacher model to help train or optimize a student model. The goals can be to reduce inference costs, compress model size, improve performance on specific tasks, or migrate certain behavioral capabilities to a new model architecture.

1. White-box Distillation

White-box distillation typically involves access to the teacher model’s internal information, such as:

• Parameters or weights;
• Intermediate layer representations;
• Output probability distributions;
• Attention-related information;
• Hidden states of different layers.

The student model does not just learn the final output text of the teacher model; it may also fit the statistical features of the teacher model’s internal computation process. Common training objectives include minimizing the difference between output distributions or making the student model’s intermediate representations closer to the teacher’s.

White-box distillation requires a high degree of access to the teacher model, usually necessitating weight files, internal interfaces, or specially exported supervision signals. Therefore, it is distinctly different from simply calling an online API to get answers.

2. Black-box Distillation

Black-box distillation does not require access to the teacher model’s internal parameters. The trainer only needs to construct a large number of questions, submit them to the teacher model, and then collect the returned content to train the student model.

These returned contents can include:

• General Q&A;
• Classification results;
• Code examples;
• Long-text explanations;
• Multi-turn dialogues;
• Reasoning processes or intermediate conclusions;
• Corrections for incorrect answers.

From a data perspective, black-box distillation is essentially using the teacher model to generate synthetic data, which is then used to train the student model. It is not equivalent to parameter-by-parameter copying, nor does it mean the student model will fully inherit the teacher model’s internal mechanisms.

The advantage of black-box distillation is its relatively low implementation barrier, allowing for rapid expansion of training samples. The drawback is that data quality heavily depends on the teacher model, making it easy to pass down the teacher’s biases, hallucinations, formatting habits, and flawed reasoning.

IV. Pre-training vs. Post-training Should Not Be Conflated

1. Pre-training Phase

Pre-training primarily uses large-scale text, code, image, or other modal data to enable the model to learn language patterns, factual associations, knowledge representations, and general task capabilities. It is more like establishing a broad space of foundational capabilities.

The scale of pre-training data is usually massive, making it unrealistic to manually review every entry. Therefore, engineering practices generally employ methods like data deduplication, quality scoring, rule-based filtering, classifier screening, and sampling checks, but it is difficult to guarantee that all content is thoroughly cleaned.

This means pre-training data may contain:

• Duplicate or near-duplicate text;
• Low-quality auto-generated content;
• Factual errors;
• Positional biases;
• Copyright and privacy risks;
• Synthetic text generated by different models.

2. Post-training Phase

Post-training further optimizes the model’s interactive behavior and task performance on top of the base model. Common objectives include:

• Following user instructions;
• Improving Q&A quality;
• Enhancing code generation;
• Reinforcing safety boundaries;
• Optimizing output formats;
• Reducing the probability of harmful or inaccurate answers.

While the scale of post-training data may be smaller than that of pre-training data, its impact on model behavior is often more direct. Whether a model tends to use bullet points, frequently includes disclaimers, or uses fixed expressions for identity questions is often related to post-training samples and system strategies.

V. Why Data Contamination is Hard to Avoid

Synthetic data is not inherently harmful. High-quality synthetic data can be used to supplement scarce domain samples, generate edge cases, construct automated evaluation sets, and even help train lightweight models.

The real problem lies in the fact that if there is a lack of reliable source labeling and quality control, model-generated data may be mistaken for real data and re-enter the training pipeline, forming a closed loop.

This closed loop can bring several types of risks:

1. Error Repetition Propagation

After an incorrect answer generated by the teacher model is learned by the student model, the student model generates similar answers, ultimately causing the same error to repeatedly appear across multiple datasets and models.

2. Expression Style Shrinkage

If training data largely comes from a few models, language expression may become increasingly homogenized. Originally rich writing styles, domain terminology, and regional expressions are gradually compressed.

3. Decline in Fact Density

Model-generated text usually has good coherence, but coherence does not equal truth. When a large amount of synthetic text lacking factual verification enters the training set, it can reduce the effective information density of the data.

4. Identity Statement Contamination

If fixed answers like “I am model X” repeatedly appear in the training corpus, other models may learn similar expressions. At this point, the model’s identity response is more like a language pattern rather than a reliable internal self-check result.

VI. How to Determine the Real Cause Behind Identity Misidentification

When faced with such phenomena, it is recommended to adopt reproducible and verifiable analytical methods rather than drawing conclusions based on a single screenshot.

1. Fix the Testing Environment

Record the complete system prompt, user prompt, model version, sampling parameters, context length, and response time. Server-side routing changes, hidden prompts, or session states can all affect the results.

2. Conduct Repeated Multi-round Tests

Use at least multiple sets of equivalent questions and repeat the tests in different sessions to observe whether the identity response is stable. If it only occurs once and disappears when the question is phrased differently, it is more likely to be random generation or context-induced.

3. Use Control Questions

You can ask respectively:

• What model are you?
• Can you confirm your training sources?
• What is your knowledge cutoff date?
• Can you access your own weights?
• Why did you make that previous identity judgment?

These questions cannot grant the model true internal permissions, but they can help determine whether its answers are consistent.

4. Do Not Treat Chain of Thought as Factual Evidence

The “reasoning process” demonstrated by the model may also be a post-hoc generated explanation and does not necessarily correspond to the true internal computation path. Identity judgments, training sources, and model attribution should rely as much as possible on public technical documentation, weight information, API descriptions, experimental reproduction, and independent evaluations, rather than on the model’s self-reporting.

VII. Clarifying the Myth: “High-Capability Models Don’t Learn from Low-Capability Models”

Distillation does not equate to a simple transfer of capability rankings. Whether a model is suitable as a teacher depends not only on overall capability but also on specific tasks.

One model might be stronger in mathematical reasoning, while another might have advantages in Chinese expression, code completion, domain knowledge, or format control. Even if the teacher model is weaker overall, it might still provide valuable samples for a specific niche task.

Therefore, the idea that “higher-capability models do not need to learn from lower-capability models” is an oversimplification. Model training focuses on task distribution, data quality, cost, and objective functions, rather than simple brand names or overall rankings.

VIII. Rational Conclusion

When a model claims to be another model, it should first be treated as a generative phenomenon requiring explanation, not as an authentication result. Similar responses may stem from public corpora, instruction tuning, black-box distillation, synthetic data loops, hidden system configurations, or random sampling.

More rigorous judgments should follow three principles:

1. Treat model self-reports as clues, not as evidence.
2. Treat output similarity as a statistical phenomenon, not directly equating it to technical copying.
3. Treat distillation as a standard training method in the industry, while paying attention to data authorization, quality control, and fact-checking.

For enterprises and developers, when building a model evaluation system, the focus should be on reproducibility, source traceability, data deduplication, synthetic data labeling, factual consistency, and cross-version stability. Only by combining these engineering aspects can one more accurately determine whether a model’s behavior originates from training data, post-training strategies, service configurations, or mere probabilistic generation.