Verified70% confidenceFactTime unknown
Large language models encountered a large volume of Markdown-formatted text from sources such as GitHub, technical documentation, and Wikipedia during the pre-training phase, and therefore have an extremely strong understanding of Markdown structure.
4
Sources
70%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedMarkdown has become the de facto standard format for LLM data pipelines, due to its high plain-text token efficiency, preservation of document semantic structure, and the large volume of Markdown content included in mainstream LLM training data.73% similarVerifiedLarge language models' performance in code generation is highly dependent on the scale and quality of pre-training data.72% similarVerifiedMarkdown is both human-readable and easy for large language models to parse, meeting the needs of both human authoring and machine reading.70% similarUnverifiedLarge language models have encountered vast amounts of Photoshop-related tutorials, source code, and UI descriptions in their training data, and are therefore able to generate structurally sound related code with relatively high confidence.69% similarUnverified大语言模型在预训练阶段接触了大量包含Base64、ROT13等编码的文本数据，因此具备解码能力，而前端安全过滤器通常以明文检测敏感内容，形成信息不对称攻击向量68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/21292API
curl https://kongchang.com/api/v1/knowledge/claims/21292MCP
get_claim(id=21292)