Unverified50% confidenceFactExact time
Code-mixing phenomena can cause significant performance degradation in existing pre-trained language models.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe structural weakness of large language models is that they optimize for 'code that looks correct' rather than 'code that meets the requirements of the current stage,' and they replicate high-frequency patterns from training data.79% similarUnverifiedLarge language models exhibit an 'instruction drift' phenomenon, where the model's probability of following early instructions gradually decreases as conversation turns increase and the context window is filled with large amounts of code.77% similarUnverifiedCode drift in pure prompt-based development is rooted in the statelessness of large language models, which re-infer system intent with each new prompt72% similarUnverified主流扩散模型的文字生成依赖CLIP等视觉-语言预训练模型，其训练数据以英文为主，导致中文字符生成困难72% similarUnverifiedDiffusion language models are still at an early stage and continue to lag behind mature autoregressive models in generation quality, long-text coherence, and training stability.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/901428API
curl https://kongchang.com/api/v1/knowledge/claims/901428MCP
get_claim(id=901428)