Model Identity Data Pollution / 模型身份数据污染

Model identity data pollution is the pattern in E246｜何谓蒸馏？聊聊硅谷如何看中国开放模型逼近前沿 where a model may say “I am Claude” or “I am ChatGPT” because internet training data contains many model outputs, prompts, transcripts, and copied conversations. [[WangTiezhen|王铁镇]] argues that this can happen even without deliberate Model Distillation / 模型蒸馏 from a specific closed model.

The concept is useful as an evidence-quality warning. Identity confusion can indicate messy or contaminated pretraining data, weak system-prompt conditioning, or reused public assistant text, but the source says it does not by itself prove that a model’s main reasoning capability was copied from another lab.

Key Claims

• Model self-identification is not a reliable provenance test.
• Public internet data increasingly contains outputs from many AI systems, making model identity text easy to absorb during pretraining.
• Removing or weakening a system prompt can reveal identity confusion even in models not being accused of distillation.
• Provenance claims need stronger evidence such as training data access, account-call traces, model behavior audits, or reproducible evaluation.

Connections

• Model Distillation / 模型蒸馏 - technical debate that identity confusion should not replace.
• Kimi K3, Claude, and ChatGPT - model names used in the source’s examples.
• AI Training Data Scarcity, Data As Education, and Model Collapse - broader data-quality and model-output recycling context.
• AI Answer Evaluation and Output Quality Gates - evaluation practices that need better evidence than surface self-labeling.