Unverified50% confidenceBenchmarkExact time
After training the integrator only on high-resource languages and transferring it to languages unseen during training, the researchers achieved a retention rate as high as 0.99.
1
Sources
50%
Confidence
Long-term
Relevance
9/24/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedGlobally, there are no more than a few hundred researchers with large-scale language model pretraining experience, making supply extremely scarce.74% similarUnverified多语言预训练模型如mBERT、XLM-R展现出跨语言零样本迁移能力，即便某些语言训练中从未与其他语言平行语料共同出现70% similarVerifiedThe safety alignment of many models degrades significantly in non-English languages, and low-resource languages are especially easy to exploit because the human annotation data for RLHF alignment training is highly concentrated in English.67% similarUnverified斯坦福HAI研究所2023年报告指出，大型语言模型经过特定行业数据训练后，其生成能力可在零样本条件下迁移至相邻垂直领域67% similarUnverifiedCompared to training or fine-tuning a model, the barrier to writing Skills is lower because it is essentially still a natural-language prompt.67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/946227API
curl https://kongchang.com/api/v1/knowledge/claims/946227MCP
get_claim(id=946227)