Unverified50% confidenceFactExact time
LLM training corpora are mixed with large amounts of code under different licenses, and generated output may inadvertently reproduce snippets of GPL or other copyleft-licensed code.
1
Sources
50%
Confidence
Long-term
Relevance
10/25/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLLM在生成代码时倾向于'重写'而非'编辑'，导致连带修改问题频繁发生68% similarUnverified经过指令微调和RLHF训练的模型在遵循格式规范方面表现更好，有时反而会生成格式更完美的虚假引用67% similarUnverifiedDiscrete masked diffusion draws on BERT's masked language modeling idea—randomly masking and restoring sequences during training and iteratively filling in from a fully masked sequence at inference—with representative works including MDLM and SEDD.66% similarVerifiedThe root cause of prompt injection lies in the fact that LLM architecture encodes system prompts, user inputs, and externally retrieved content uniformly into the same continuous token sequence, making it impossible for the model to distinguish trusted instructions from untrusted data at the semantic level.64% similarUnverified模型的格式方言源于指令微调阶段训练数据的格式差异，被模型深度内化形成强烈格式偏好，难以通过运行时提示词完全覆盖64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1097005API
curl https://kongchang.com/api/v1/knowledge/claims/1097005MCP
get_claim(id=1097005)